{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "bsoid_v1.2.1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "md9PWmPWsVar",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4675beed-3bd0-4254-90ab-51fd446d2075"
      },
      "source": [
        "#@title B-SOiD GOOGLE COLAB v1.2.1 \n",
        "# Created by Yttri Lab at Carnegie Mellon University.\n",
        "# Program developer: Alexander Hsu.\n",
        "# Program collaborator: Vishal Patel.\n",
        "# Date last modified: 041820\n",
        "# Contact: ahsu2@andrew.cmu.edu\n",
        "\n",
        "# Import necessary python packages\n",
        "\n",
        "import glob\n",
        "import logging\n",
        "import math\n",
        "import os\n",
        "import re\n",
        "import sys\n",
        "import time\n",
        "\n",
        "import joblib\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from matplotlib.axes._axes import _log as matplotlib_axes_logger\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sn\n",
        "\n",
        "!pip install bhtsne\n",
        "\n",
        "from bhtsne import tsne\n",
        "from sklearn import mixture, svm\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tqdm import tqdm\n",
        "\n",
        "matplotlib_axes_logger.setLevel('ERROR')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: bhtsne in /usr/local/lib/python3.6/dist-packages (0.1.9)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.6/dist-packages (from bhtsne) (0.29.17)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from bhtsne) (1.18.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4K6C2m8kuXLA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "logging.basicConfig(\n",
        "    format='%(asctime)s %(levelname)-8s %(message)s',\n",
        "    level='INFO',\n",
        "    datefmt='%Y-%m-%d %H:%M:%S',\n",
        "    stream=sys.stdout)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8G-BCGQsrsr",
        "colab_type": "text"
      },
      "source": [
        "# Load your pose estimate data (.csv) generated from either [DeepLabCut](https://github.com/AlexEMG/DeepLabCut)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXoOJXWsa76S",
        "colab_type": "code",
        "outputId": "8880ec18-a7b4-43cc-a29a-c504ba992ed4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive/\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQfqmwMoseFL",
        "colab_type": "text"
      },
      "source": [
        "# Defining *parameters* based on your data.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sox5PBTm5zIQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load your data\n",
        "# Step 1: change BASE_PATH to \"/content/drive/My Drive/DeepLabCut/\"\n",
        "# Step 2: change TRAIN_FOLDERS to [\"experiment1\",\"experiment2\",\"more\"]\n",
        "BASE_PATH = \"/content/drive/My Drive/Colab Notebooks/\"\n",
        "TRAIN_FOLDERS = [\"041919\"]\n",
        "PREDICT_FOLDERS = [\"041919\"]  # Folder paths containing new dataset to predict using built classifier.\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzu2oRw8wNsg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Minimum is the 6 I listed below. Please order them based on your csv orders.\n",
        "# Can have more, but not less.\n",
        "BODYPARTS = {\n",
        "    'Snout/Head': 0,\n",
        "    'Neck': None,\n",
        "    'Forepaw/Shoulder1': 1,\n",
        "    'Forepaw/Shoulder2': 2,\n",
        "    'Bodycenter': None,\n",
        "    'Hindpaw/Hip1': 3,\n",
        "    'Hindpaw/Hip2': 4,\n",
        "    'Tailbase': 5,\n",
        "    'Tailroot': None\n",
        "}\n",
        "\n",
        "# note that you can use a different number for new data as long as the video is same scale/view\n",
        "FPS = 60  # Frame-rate of your video,\n",
        "\n",
        "# IF YOU'D LIKE TO SKIP PLOTTING/CREATION OF VIDEOS, change below plot settings to False\n",
        "PLOT_TRAINING = True\n",
        "GEN_VIDEOS = False # set this to true only if you have sufficient drive space\n",
        "\n",
        "# Output directory to where you want the analysis to be stored\n",
        "OUTPUT_PATH = '/content/drive/My Drive/Colab Notebooks/'\n",
        "MODEL_NAME = 'c57bl6_n2_120min' # Your machine learning model name\n",
        "\n",
        "# IF GEN_VIDEOS = True\n",
        "# Pick the video\n",
        "VID_NAME = '/content/drive/My Drive/Colab Notebooks/041919/2019-04-19_09-34-36cut0_30min.mp4'\n",
        "# What number would be video be in terms of prediction order? (0=file 1/folder1, 1=file2/folder 1, etc.)\n",
        "ID = 0\n",
        "# Create a folder to store extracted images and videos make sure these folders exist.\n",
        "# This program will predict labels and print them on these images\n",
        "FRAME_DIR = '/content/drive/My Drive/Colab Notebooks/041919/0_30min_10fpsPNGs'\n",
        "# In addition, this will also create an entire sample group videos for ease of understanding\n",
        "SHORTVID_DIR = '/content/drive/My Drive/Colab Notebooks/examples'\n",
        "\n",
        "###########################################################################################\n",
        "######## UNLESS YOU WANT TO TUNE B-SOID PARAMETERS, LEAVE THE FOLLOWING TO DEFAULT ########\n",
        "###########################################################################################\n",
        "COMP = 1  # COMP = 1: Train one classifier for all CSV files; COMP = 0: Classifier/CSV file.\n",
        "\n",
        "# Expectation Maximization: Mixture models parameters\n",
        "EMGMM_PARAMS = {\n",
        "    'n_components': 30,\n",
        "    'covariance_type': 'full', # t-SNE has no structure\n",
        "    'tol': 0.001,\n",
        "    'reg_covar': 1e-06,\n",
        "    'max_iter': 100,\n",
        "    'n_init': 10, # 10 iterations to escape poor initialization\n",
        "    'init_params': 'random', # random initialization\n",
        "    'random_state': 23,\n",
        "    'verbose': 1 # set this to 0 if you don't want to show progress for em-gmm.\n",
        "}\n",
        "\n",
        "# SVM parameters\n",
        "SVM_PARAMS = {\n",
        "    'gamma': 0.5,  # Kernel coefficient\n",
        "    'C': 10,  # Regularization parameter\n",
        "    'probability': True,\n",
        "    'random_state': 23,\n",
        "    'verbose': 0 # set this to 1 if you want to show optimization progress\n",
        "}\n",
        "\n",
        "HLDOUT = 0.2  # Test partition ratio to validate clustering separation.\n",
        "CV_IT = 10  # Number of iterations for cross-validation to show it's not over-fitting."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_boK1sph_2a",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Define all the *functions* necessary.\n",
        "def boxcar_center(a, n):\n",
        "\n",
        "  a1 = pd.Series(a)\n",
        "  moving_avg = np.array(a1.rolling(window = n,min_periods=1,center = True).mean())\n",
        "  \n",
        "  return moving_avg \n",
        "\n",
        "\n",
        "def convert_int(s):\n",
        "    \"\"\" Converts digit string to integer\n",
        "    \"\"\"\n",
        "    if s.isdigit():\n",
        "        return int(s)\n",
        "    else:\n",
        "        return s\n",
        "\n",
        "\n",
        "def alphanum_key(s):\n",
        "    \"\"\" Turn a string into a list of string and number chunks.\n",
        "        \"z23a\" -> [\"z\", 23, \"a\"]\n",
        "    \"\"\"\n",
        "    return [convert_int(c) for c in re.split('([0-9]+)', s)]\n",
        "\n",
        "\n",
        "def sort_nicely(l):\n",
        "    \"\"\" Sort the given list in the way that humans expect.\n",
        "    \"\"\"\n",
        "    l.sort(key=alphanum_key)\n",
        "\n",
        "\n",
        "def get_filenames(folder):\n",
        "    \"\"\"\n",
        "    Gets a list of filenames within a folder\n",
        "    :param folder: str, folder path\n",
        "    :return: list, filenames\n",
        "    \"\"\"\n",
        "    filenames = glob.glob(BASE_PATH + folder + '/*.csv')\n",
        "    sort_nicely(filenames)\n",
        "    return filenames\n",
        "\n",
        "\n",
        "def adp_filt(currdf: object):\n",
        "    \"\"\"\n",
        "    :param currdf: object, csv data frame\n",
        "    :return currdf_filt: 2D array, filtered data\n",
        "    :return perc_rect: 1D array, percent filtered per BODYPART\n",
        "    \"\"\"\n",
        "    lIndex = []\n",
        "    xIndex = []\n",
        "    yIndex = []\n",
        "    currdf = np.array(currdf[1:])\n",
        "    for header in range(len(currdf[0])):\n",
        "        if currdf[0][header] == \"likelihood\":\n",
        "            lIndex.append(header)\n",
        "        elif currdf[0][header] == \"x\":\n",
        "            xIndex.append(header)\n",
        "        elif currdf[0][header] == \"y\":\n",
        "            yIndex.append(header)\n",
        "    logging.info('Extracting likelihood value...')\n",
        "    currdf = np.array(currdf)\n",
        "    curr_df1 = currdf[:, 1:]\n",
        "    datax = curr_df1[:, np.array(xIndex) - 1]\n",
        "    datay = curr_df1[:, np.array(yIndex) - 1]\n",
        "    data_lh = curr_df1[:, np.array(lIndex) - 1]\n",
        "    currdf_filt = np.zeros((datax.shape[0] - 1, (datax.shape[1]) * 2))\n",
        "    perc_rect = []\n",
        "    logging.info('Computing data threshold to forward fill any sub-threshold (x,y)...')\n",
        "    for i in range(data_lh.shape[1]):\n",
        "        perc_rect.append(0)\n",
        "    for x in tqdm(range(data_lh.shape[1])):\n",
        "        a, b = np.histogram(data_lh[1:, x].astype(np.float))\n",
        "        rise_a = np.where(np.diff(a) >= 0)\n",
        "        if rise_a[0][0] > 1:\n",
        "            llh = (b[rise_a[0][0]+1])\n",
        "        else:\n",
        "            llh = (b[rise_a[0][1]+1])\n",
        "        data_lh_float = data_lh[1:, x].astype(np.float)\n",
        "        perc_rect[x] = np.sum(data_lh_float < llh) / data_lh.shape[0]\n",
        "        for i in range(1, data_lh.shape[0] - 1):\n",
        "            if data_lh_float[i] < llh:\n",
        "                currdf_filt[i, (2 * x):(2 * x + 2)] = currdf_filt[i - 1, (2 * x):(2 * x + 2)]\n",
        "            else:\n",
        "                currdf_filt[i, (2 * x):(2 * x + 2)] = np.hstack([datax[i, x], datay[i, x]])\n",
        "    currdf_filt = np.array(currdf_filt[1:])\n",
        "    currdf_filt = currdf_filt.astype(np.float)\n",
        "    return currdf_filt, perc_rect\n",
        "\n",
        "\n",
        "def import_folders(folders: list):\n",
        "    \"\"\"\n",
        "    Import multiple folders containing .csv files and process them\n",
        "    :param folders: list, data folders\n",
        "    :return filenames: list, data filenames\n",
        "    :return data: list, filtered csv data\n",
        "    :return perc_rect_li: list, percent filtered\n",
        "    \"\"\"\n",
        "    filenames = []\n",
        "    rawdata_li = []\n",
        "    data_li = []\n",
        "    perc_rect_li = []\n",
        "    for i, fd in enumerate(folders):  # Loop through folders\n",
        "        f = get_filenames(fd)\n",
        "        for j, filename in enumerate(f):\n",
        "            logging.info('Importing CSV file {} from folder {}'.format(j + 1, i + 1))\n",
        "            curr_df = pd.read_csv(filename, low_memory=False)\n",
        "            curr_df_filt, perc_rect = adp_filt(curr_df)\n",
        "            logging.info('Done preprocessing (x,y) from file {}, folder {}.'.format(j + 1, i + 1))\n",
        "            rawdata_li.append(curr_df)\n",
        "            perc_rect_li.append(perc_rect)\n",
        "            data_li.append(curr_df_filt)\n",
        "        filenames.append(f)\n",
        "        logging.info('Processed {} CSV files from folder: {}'.format(len(f), fd))\n",
        "    data = np.array(data_li)\n",
        "    logging.info('Processed a total of {} CSV files, and compiled into a {} data list.'.format(len(data_li),\n",
        "                                                                                               data.shape))\n",
        "    return filenames, data, perc_rect_li\n",
        "\n",
        "\n",
        "def likelihoodprocessing(folders):\n",
        "    \"\"\"\n",
        "    :param folders: list, data folders\n",
        "    :return filenames: list, data filenames\n",
        "    :return data: list, filtered data list\n",
        "    :retrun perc_rect: 1D array, percent filtered per BODYPART\n",
        "    \"\"\"\n",
        "    filenames, data, perc_rect = import_folders(folders)\n",
        "    return filenames, data, perc_rect\n",
        "\n",
        "\n",
        "def plot_classes(data, assignments):\n",
        "    \"\"\" Plot trained_tsne for EM-GMM assignments\n",
        "    :param data: 2D array, trained_tsne\n",
        "    :param assignments: 1D array, EM-GMM assignments\n",
        "    \"\"\"\n",
        "    uk = list(np.unique(assignments))\n",
        "    R = np.linspace(0, 1, len(uk))\n",
        "    cmap = plt.cm.get_cmap(\"Spectral\")(R)\n",
        "    tsne_x, tsne_y, tsne_z = data[:, 0], data[:, 1], data[:, 2]\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "    for g in np.unique(assignments):\n",
        "        idx = np.where(np.array(assignments) == g)\n",
        "        ax.scatter(tsne_x[idx], tsne_y[idx], tsne_z[idx], c=cmap[g],\n",
        "                   label=g, s=0.5, marker='o', alpha=0.8)\n",
        "    ax.set_xlabel('Dim. 1')\n",
        "    ax.set_ylabel('Dim. 2')\n",
        "    ax.set_zlabel('Dim. 3')\n",
        "    ax.view_init(70, 135)\n",
        "    plt.title('Assignments by GMM')\n",
        "    plt.legend(ncol=3)\n",
        "    plt.show()\n",
        "    timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "    my_file = 'train_assignments_'\n",
        "    fig.savefig(os.path.join(OUTPUT_PATH, str.join('', (my_file, timestr, '.svg'))))\n",
        "\n",
        "\n",
        "def plot_accuracy(scores):\n",
        "    \"\"\"\n",
        "    :param scores: 1D array, cross-validated accuracies for SVM classifier.\n",
        "    \"\"\"\n",
        "    fig = plt.figure(facecolor='w', edgecolor='k')\n",
        "    fig.suptitle(\"Performance on {} % data\".format(HLDOUT * 100))\n",
        "    ax = fig.add_subplot(111)\n",
        "    ax.boxplot(scores, notch=None)\n",
        "    x = np.random.normal(1, 0.04, size=len(scores))\n",
        "    plt.scatter(x, scores, s=40, c='r', alpha=0.5)\n",
        "    ax.set_xlabel('SVM RBF classifier')\n",
        "    ax.set_ylabel('Accuracy')\n",
        "    plt.show()\n",
        "    timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "    my_file = 'clf_scores_'\n",
        "    fig.savefig(os.path.join(OUTPUT_PATH, str.join('', (my_file, timestr, '.svg'))))\n",
        "\n",
        "\n",
        "def plot_tmat(tm: object, fps):\n",
        "    \"\"\"\n",
        "    :param tm: object, transition matrix data frame\n",
        "    :param fps: scalar, camera frame-rate\n",
        "    \"\"\"\n",
        "    timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "    fig = plt.figure()\n",
        "    fig.suptitle(\"Transition matrix of {} behaviors\".format(tm.shape[0]))\n",
        "    sn.heatmap(tm, annot=True)\n",
        "    plt.xlabel(\"Next frame behavior\")\n",
        "    plt.ylabel(\"Current frame behavior\")\n",
        "    plt.show()\n",
        "    my_file = 'transition_matrix_'\n",
        "    fig.savefig(os.path.join(OUTPUT_PATH, str.join('', (my_file, str(fps), timestr, '.svg'))))\n",
        "    return\n",
        "\n",
        "\n",
        "def plot_feats(feats: list, labels: list):\n",
        "    \"\"\"\n",
        "    :param feats: list, features for multiple sessions\n",
        "    :param labels: list, labels for multiple sessions\n",
        "    \"\"\"\n",
        "    result = isinstance(labels, list)\n",
        "    timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "    if result:\n",
        "        for k in range(0, len(feats)):\n",
        "            labels_k = np.array(labels[k])\n",
        "            feats_k = np.array(feats[k])\n",
        "            R = np.linspace(0, 1, len(np.unique(labels_k)))\n",
        "            color = plt.cm.get_cmap(\"Spectral\")(R)\n",
        "            feat_ls = (\"Relative snout to forepaws placement\", \"Relative snout to hind paws placement\",\n",
        "                       \"Inter-forepaw distance\", \"Body length\", \"Body angle\",\n",
        "                       \"Snout displacement\", \"Tail-base displacement\")\n",
        "            for j in range(0, feats_k.shape[0]):\n",
        "                fig = plt.figure(facecolor='w', edgecolor='k')\n",
        "                for i in range(0, len(np.unique(labels_k))):\n",
        "                    plt.subplot(len(np.unique(labels_k)), 1, i + 1)\n",
        "                    if j == 2 or j == 3 or j == 5 or j == 6:\n",
        "                        plt.hist(feats_k[j, labels_k == i],\n",
        "                                 bins=np.linspace(0, np.mean(feats_k[j, :]) + 3 * np.std(feats_k[j, :]), num=50),\n",
        "                                 range=(0, np.mean(feats_k[j, :]) + 3 * np.std(feats_k[j, :])),\n",
        "                                 color=color[i], density=True)\n",
        "                        fig.suptitle(\"{} pixels\".format(feat_ls[j]))\n",
        "                        plt.xlim(0, np.mean(feats_k[j, :]) + 3 * np.std(feats_k[j, :]))\n",
        "                        if i < len(np.unique(labels_k)) - 1:\n",
        "                            plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
        "                    else:\n",
        "                        plt.hist(feats_k[j, labels_k == i],\n",
        "                                 bins=np.linspace(np.mean(feats_k[j, :]) - 3 * np.std(feats_k[j, :]),\n",
        "                                                  np.mean(feats_k[j, :]) + 3 * np.std(feats_k[j, :]), num=50),\n",
        "                                 range=(np.mean(feats_k[j, :]) - 3 * np.std(feats_k[j, :]),\n",
        "                                        np.mean(feats_k[j, :]) + 3 * np.std(feats_k[j, :])),\n",
        "                                 color=color[i], density=True)\n",
        "                        plt.xlim(np.mean(feats_k[j, :]) - 3 * np.std(feats_k[j, :]),\n",
        "                                 np.mean(feats_k[j, :]) + 3 * np.std(feats_k[j, :]))\n",
        "                        fig.suptitle(\"{} pixels\".format(feat_ls[j]))\n",
        "                        if i < len(np.unique(labels_k)) - 1:\n",
        "                            plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
        "                my_file = 'sess{}_feat{}_hist_'.format(k + 1, j + 1)\n",
        "                fig.savefig(os.path.join(OUTPUT_PATH, str.join('', (my_file, timestr, '.svg'))))\n",
        "            plt.show()\n",
        "    else:\n",
        "        R = np.linspace(0, 1, len(np.unique(labels)))\n",
        "        color = plt.cm.get_cmap(\"Spectral\")(R)\n",
        "        feat_ls = (\"Relative snout to forepaws placement\", \"Relative snout to hind paws placement\",\n",
        "                   \"Inter-forepaw distance\", \"Body length\", \"Body angle\",\n",
        "                   \"Snout displacement\", \"Tail-base displacement\")\n",
        "        for j in range(0, feats.shape[0]):\n",
        "            fig = plt.figure(facecolor='w', edgecolor='k')\n",
        "            for i in range(0, len(np.unique(labels))):\n",
        "                plt.subplot(len(np.unique(labels)), 1, i + 1)\n",
        "                if j == 2 or j == 3 or j == 5 or j == 6:\n",
        "                    plt.hist(feats[j, labels == i],\n",
        "                             bins=np.linspace(0, np.mean(feats[j, :]) + 3 * np.std(feats[j, :]), num=50),\n",
        "                             range=(0, np.mean(feats[j, :]) + 3 * np.std(feats[j, :])),\n",
        "                             color=color[i], density=True)\n",
        "                    fig.suptitle(\"{} pixels\".format(feat_ls[j]))\n",
        "                    plt.xlim(0, np.mean(feats[j, :]) + 3 * np.std(feats[j, :]))\n",
        "                    if i < len(np.unique(labels)) - 1:\n",
        "                        plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
        "                else:\n",
        "                    plt.hist(feats[j, labels == i],\n",
        "                             bins=np.linspace(np.mean(feats[j, :]) - 3 * np.std(feats[j, :]),\n",
        "                                              np.mean(feats[j, :]) + 3 * np.std(feats[j, :]), num=50),\n",
        "                             range=(np.mean(feats[j, :]) - 3 * np.std(feats[j, :]),\n",
        "                                    np.mean(feats[j, :]) + 3 * np.std(feats[j, :])),\n",
        "                             color=color[i], density=True)\n",
        "                    plt.xlim(np.mean(feats[j, :]) - 3 * np.std(feats[j, :]),\n",
        "                             np.mean(feats[j, :]) + 3 * np.std(feats[j, :]))\n",
        "                    fig.suptitle(\"{} pixels\".format(feat_ls[j]))\n",
        "                    if i < len(np.unique(labels)) - 1:\n",
        "                        plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
        "            my_file = 'feat{}_hist_'.format(j + 1)\n",
        "            fig.savefig(os.path.join(OUTPUT_PATH, str.join('', (my_file, timestr, '.svg'))))\n",
        "        plt.show()\n",
        "\n",
        "\n",
        "def transition_matrix(labels):\n",
        "    \"\"\"\n",
        "    :param labels: 1D array, predicted labels\n",
        "    :return df_tm: object, transition matrix data frame\n",
        "    \"\"\"\n",
        "    n = 1 + max(labels)\n",
        "    tm = [[0] * n for _ in range(n)]\n",
        "    for (i, j) in zip(labels, labels[1:]):\n",
        "        tm[i][j] += 1\n",
        "    for row in tm:\n",
        "        s = sum(row)\n",
        "        if s > 0:\n",
        "            row[:] = [f / s for f in row]\n",
        "    df_tm = pd.DataFrame(tm)\n",
        "    return df_tm\n",
        "\n",
        "\n",
        "def rle(inarray):\n",
        "    \"\"\" run length encoding. Partial credit to R rle function.\n",
        "        Multi datatype arrays catered for including non Numpy\n",
        "        returns: tuple (runlengths, startpositions, values) \"\"\"\n",
        "    ia = np.asarray(inarray)  # force numpy\n",
        "    n = len(ia)\n",
        "    if n == 0:\n",
        "        return (None, None, None)\n",
        "    else:\n",
        "        y = np.array(ia[1:] != ia[:-1])  # pairwise unequal (string safe)\n",
        "        i = np.append(np.where(y), n - 1)  # must include last element posi\n",
        "        z = np.diff(np.append(-1, i))  # run lengths\n",
        "        p = np.cumsum(np.append(0, z))[:-1]  # positions\n",
        "        return z, p, ia[i]\n",
        "\n",
        "\n",
        "def behv_time(labels):\n",
        "    \"\"\"\n",
        "    :param labels: 1D array, predicted labels\n",
        "    :return beh_t: 1D array, percent time for each label\n",
        "    \"\"\"\n",
        "    beh_t = []\n",
        "    for i in range(0, len(np.unique(labels))):\n",
        "        t = np.sum(labels == i) / labels.shape[0]\n",
        "        beh_t.append(t)\n",
        "    return beh_t\n",
        "\n",
        "\n",
        "def behv_dur(labels):\n",
        "    \"\"\"\n",
        "    :param labels: 1D array, predicted labels\n",
        "    :return runlen_df: object, behavioral duration run lengths data frame\n",
        "    :return dur_stats: object, behavioral duration statistics data frame\n",
        "    \"\"\"\n",
        "    lengths, pos, grp = rle(labels)\n",
        "    df_lengths = pd.DataFrame(lengths, columns={'Run lengths'})\n",
        "    df_grp = pd.DataFrame(grp, columns={'B-SOiD labels'})\n",
        "    df_pos = pd.DataFrame(pos, columns={'Start time (frames)'})\n",
        "    runlengths = [df_grp, df_pos, df_lengths]\n",
        "    runlen_df = pd.concat(runlengths, axis=1)\n",
        "    beh_t = behv_time(labels)\n",
        "    dur_means = []\n",
        "    dur_quant0 = []\n",
        "    dur_quant1 = []\n",
        "    dur_quant2 = []\n",
        "    dur_quant3 = []\n",
        "    dur_quant4 = []\n",
        "    for i in range(0, len(np.unique(grp))):\n",
        "        try:\n",
        "            dur_means.append(np.mean(lengths[np.where(grp == i)]))\n",
        "            dur_quant0.append(np.quantile(lengths[np.where(grp == i)], 0.1))\n",
        "            dur_quant1.append(np.quantile(lengths[np.where(grp == i)], 0.25))\n",
        "            dur_quant2.append(np.quantile(lengths[np.where(grp == i)], 0.5))\n",
        "            dur_quant3.append(np.quantile(lengths[np.where(grp == i)], 0.75))\n",
        "            dur_quant4.append(np.quantile(lengths[np.where(grp == i)], 0.9))\n",
        "        except:\n",
        "            # dur_means.append(0)\n",
        "            dur_quant0.append(0)\n",
        "            dur_quant1.append(0)\n",
        "            dur_quant2.append(0)\n",
        "            dur_quant3.append(0)\n",
        "            dur_quant4.append(0)\n",
        "    alldata = np.concatenate([np.array(beh_t).reshape(len(np.array(beh_t)), 1),\n",
        "                              np.array(dur_means).reshape(len(np.array(dur_means)), 1),\n",
        "                              np.array(dur_quant0).reshape(len(np.array(dur_quant0)), 1),\n",
        "                              np.array(dur_quant1).reshape(len(np.array(dur_quant1)), 1),\n",
        "                              np.array(dur_quant2).reshape(len(np.array(dur_quant2)), 1),\n",
        "                              np.array(dur_quant3).reshape(len(np.array(dur_quant3)), 1),\n",
        "                              np.array(dur_quant4).reshape(len(np.array(dur_quant4)), 1)], axis=1)\n",
        "    micolumns = pd.MultiIndex.from_tuples([('Stats', 'Percent of time'),\n",
        "                                           ('', 'Mean duration (frames)'), ('', '10th %tile (frames)'),\n",
        "                                           ('', '25th %tile (frames)'), ('', '50th %tile (frames)'),\n",
        "                                           ('', '75th %tile (frames)'), ('', '90th %tile (frames)')],\n",
        "                                          names=['', 'B-SOiD labels'])\n",
        "    dur_stats = pd.DataFrame(alldata, columns=micolumns)\n",
        "    return runlen_df, dur_stats\n",
        "\n",
        "\n",
        "def bsoid_stats(labels, output_path=OUTPUT_PATH):\n",
        "    \"\"\"\n",
        "    :param labels: 1D array: predicted labels\n",
        "    :param output_path: string, output directory\n",
        "    :return dur_stats: object, behavioral duration statistics data frame\n",
        "    :return tm: object, transition matrix data frame\n",
        "    \"\"\"\n",
        "    runlen_df, dur_stats = behv_dur(labels)\n",
        "    tm = transition_matrix(labels)\n",
        "    return runlen_df, dur_stats, tm\n",
        "\n",
        "\n",
        "def get_vidnames(folder):\n",
        "    \"\"\"\n",
        "    Gets a list of filenames within a folder\n",
        "    :param folder: str, folder path\n",
        "    :return: list, video filenames\n",
        "    \"\"\"\n",
        "    vidnames = glob.glob(BASE_PATH + folder + '/*.mp4')\n",
        "    sort_nicely(vidnames)\n",
        "    return vidnames\n",
        "\n",
        "\n",
        "def vid2frame(vidname, labels, fps, output_path=FRAME_DIR):\n",
        "    \"\"\"\n",
        "    Extracts frames every 100ms to match the labels for visualizations\n",
        "    :param vidname: string, path to video\n",
        "    :param labels: 1D array, labels from training\n",
        "    :param fps: scalar, frame-rate of original camera\n",
        "    :param output_path: string, path to output\n",
        "    \"\"\"\n",
        "    vidobj = cv2.VideoCapture(vidname)\n",
        "    pbar = tqdm(total=int(vidobj.get(cv2.CAP_PROP_FRAME_COUNT)))\n",
        "    width = vidobj.get(3)\n",
        "    height = vidobj.get(4)\n",
        "    labels = np.hstack((labels[0],labels)) # fill the first frame\n",
        "    count = 0\n",
        "    count1 = 0\n",
        "    font_scale = 1\n",
        "    font = cv2.FONT_HERSHEY_COMPLEX\n",
        "    rectangle_bgr = (0, 0, 0)\n",
        "    while vidobj.isOpened():\n",
        "        ret, frame = vidobj.read()\n",
        "        if ret:\n",
        "            text = 'Group' + str(labels[count1])\n",
        "            (text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=1)[0]\n",
        "            text_offset_x = 50\n",
        "            text_offset_y = 50\n",
        "            box_coords = ((text_offset_x - 12, text_offset_y + 12),\n",
        "                          (text_offset_x + text_width + 12, text_offset_y - text_height - 8))\n",
        "            cv2.rectangle(frame, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)\n",
        "            cv2.putText(frame, text, (text_offset_x, text_offset_y), font,\n",
        "                        fontScale=font_scale, color=(255, 255, 255), thickness=1)\n",
        "            cv2.imwrite(os.path.join(output_path, 'frame{:d}.png'.format(count1)), frame)\n",
        "            count += round(fps / 10)  # i.e. at 60fps, this skips every 5\n",
        "            count1 += 1\n",
        "            vidobj.set(1, count)\n",
        "            pbar.update(round(fps / 10))\n",
        "        else:\n",
        "            vidobj.release()\n",
        "            break\n",
        "    pbar.close()\n",
        "    return\n",
        "\n",
        "\n",
        "def import_vidfolders(folders: list, output_path: list):\n",
        "    \"\"\"\n",
        "    Import multiple folders containing .mp4 files and extract frames from them\n",
        "    :param folders: list of folder paths\n",
        "    :param output_path: list, directory to where you want to store extracted vid images in LOCAL_CONFIG\n",
        "    \"\"\"\n",
        "    vidnames = []\n",
        "    for i, fd in enumerate(folders):  # Loop through folders\n",
        "        v = get_vidnames(fd)\n",
        "        for j, vidname in enumerate(v):\n",
        "            logging.info('Extracting frames from {} and appending labels to these images...'.format(vidname))\n",
        "            vid2frame(vidname, output_path)\n",
        "            logging.info('Done extracting images and writing labels, from MP4 file {}'.format(j + 1))\n",
        "        vidnames.append(v)\n",
        "        logging.info('Processed {} MP4 files from folder: {}'.format(len(v), fd))\n",
        "    return\n",
        "\n",
        "\n",
        "def repeatingNumbers(labels):\n",
        "    \"\"\"\n",
        "    :param labels: 1D array, predicted labels\n",
        "    :return n_list: 1D array, the label number\n",
        "    :return idx: 1D array, label start index\n",
        "    :return lengths: 1D array, how long each bout lasted for\n",
        "    \"\"\"\n",
        "    i = 0\n",
        "    n_list = []\n",
        "    idx = []\n",
        "    lengths = []\n",
        "    while i < len(labels) - 1:\n",
        "        n = labels[i]\n",
        "        n_list.append(n)\n",
        "        startIndex = i\n",
        "        idx.append(i)\n",
        "        while i < len(labels) - 1 and labels[i] == labels[i + 1]:\n",
        "            i = i + 1\n",
        "        endIndex = i\n",
        "        length = endIndex - startIndex\n",
        "        lengths.append(length)\n",
        "        i = i + 1\n",
        "    return n_list, idx, lengths\n",
        "\n",
        "\n",
        "def create_labeled_vid(labels, crit=3, counts=5, frame_dir=FRAME_DIR, output_path=SHORTVID_DIR):\n",
        "    \"\"\"\n",
        "    :param labels: 1D array, labels from training or testing\n",
        "    :param crit: scalar, minimum duration for random selection of behaviors, default 300ms\n",
        "    :param counts: scalar, number of randomly generated examples, default 5\n",
        "    :param frame_dir: string, directory to where you extracted vid images in LOCAL_CONFIG\n",
        "    :param output_path: string, directory to where you want to store short video examples in LOCAL_CONFIG\n",
        "    \"\"\"\n",
        "    images = [img for img in os.listdir(frame_dir) if img.endswith(\".png\")]\n",
        "    sort_nicely(images)\n",
        "    fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
        "    frame = cv2.imread(os.path.join(frame_dir, images[0]))\n",
        "    height, width, layers = frame.shape\n",
        "    rnges = []\n",
        "    n, idx, lengths = repeatingNumbers(labels)\n",
        "    idx2 = []\n",
        "    for i, j in enumerate(lengths):\n",
        "        if j >= crit:\n",
        "            rnges.append(range(idx[i], idx[i] + j))\n",
        "            idx2.append(i)\n",
        "    for i in tqdm(range(0, len(np.unique(labels)))):\n",
        "        a = []\n",
        "        for j in range(0, len(rnges)):\n",
        "            if n[idx2[j]] == i:\n",
        "                a.append(rnges[j])\n",
        "        try:\n",
        "            rand_rnges = random.sample(a, counts)\n",
        "            for k in range(0, len(rand_rnges)):\n",
        "                video_name = 'group_{}_example_{}.mp4'.format(i, k)\n",
        "                grpimages = []\n",
        "                for l in rand_rnges[k]:\n",
        "                    grpimages.append(images[l])\n",
        "                video = cv2.VideoWriter(os.path.join(output_path, video_name), fourcc, 5, (width, height))\n",
        "                for image in grpimages:\n",
        "                    video.write(cv2.imread(os.path.join(frame_dir, image)))\n",
        "                cv2.destroyAllWindows()\n",
        "                video.release()\n",
        "        except:\n",
        "            pass\n",
        "    return\n",
        "\n",
        "\n",
        "def videoprocessing(vidname, labels, fps, output_path):\n",
        "    vid2frame(vidname, labels, fps, output_path)\n",
        "    create_labeled_vid(labels, crit=3, counts=5, frame_dir=output_path, output_path=SHORTVID_DIR)\n",
        "    return\n",
        "\n",
        "\n",
        "def bsoid_tsne(data: list, bodyparts=BODYPARTS, fps=FPS, comp=COMP):\n",
        "    \"\"\"\n",
        "    Trains t-SNE (unsupervised) given a set of features based on (x,y) positions\n",
        "    :param data: list of 3D array\n",
        "    :param bodyparts: dict, body parts with their orders in LOCAL_CONFIG\n",
        "    :param fps: scalar, argument specifying camera frame-rate in LOCAL_CONFIG\n",
        "    :param comp: boolean (0 or 1), argument to compile data or not in LOCAL_CONFIG\n",
        "    :return f_10fps: 2D array, features\n",
        "    :retrun f_10fps_sc: 2D array, standardized features\n",
        "    :return trained_tsne: 2D array, trained t-SNE space\n",
        "    \"\"\"\n",
        "    win_len = np.int(np.round(0.05 / (1 / fps)) * 2 - 1)\n",
        "    feats = []\n",
        "    for m in range(len(data)):\n",
        "        logging.info('Extracting features from CSV file {}...'.format(m + 1))\n",
        "        dataRange = len(data[m])\n",
        "        fpd = data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1'):2 * bodyparts.get('Forepaw/Shoulder1') + 2] - \\\n",
        "              data[m][:, 2 * bodyparts.get('Forepaw/Shoulder2'):2 * bodyparts.get('Forepaw/Shoulder2') + 2]\n",
        "        cfp = np.vstack(((data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1')] +\n",
        "                          data[m][:, 2 * bodyparts.get('Forepaw/Shoulder2')]) / 2,\n",
        "                         (data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1') + 1] +\n",
        "                          data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1') + 1]) / 2)).T\n",
        "        cfp_pt = np.vstack(([cfp[:, 0] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                             cfp[:, 1] - data[m][:, 2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        chp = np.vstack((((data[m][:, 2 * bodyparts.get('Hindpaw/Hip1')] +\n",
        "                           data[m][:, 2 * bodyparts.get('Hindpaw/Hip2')]) / 2),\n",
        "                         ((data[m][:, 2 * bodyparts.get('Hindpaw/Hip1') + 1] +\n",
        "                           data[m][:, 2 * bodyparts.get('Hindpaw/Hip2') + 1]) / 2))).T\n",
        "        chp_pt = np.vstack(([chp[:, 0] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                             chp[:, 1] - data[m][:, 2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        sn_pt = np.vstack(([data[m][:, 2 * bodyparts.get('Snout/Head')] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                            data[m][:, 2 * bodyparts.get('Snout/Head') + 1] - data[m][:,\n",
        "                                                                              2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        fpd_norm = np.zeros(dataRange)\n",
        "        cfp_pt_norm = np.zeros(dataRange)\n",
        "        chp_pt_norm = np.zeros(dataRange)\n",
        "        sn_pt_norm = np.zeros(dataRange)\n",
        "        for i in range(1, dataRange):\n",
        "            fpd_norm[i] = np.array(np.linalg.norm(fpd[i, :]))\n",
        "            cfp_pt_norm[i] = np.linalg.norm(cfp_pt[i, :])\n",
        "            chp_pt_norm[i] = np.linalg.norm(chp_pt[i, :])\n",
        "            sn_pt_norm[i] = np.linalg.norm(sn_pt[i, :])\n",
        "        fpd_norm_smth = boxcar_center(fpd_norm, win_len)\n",
        "        sn_cfp_norm_smth = boxcar_center(sn_pt_norm - cfp_pt_norm, win_len)\n",
        "        sn_chp_norm_smth = boxcar_center(sn_pt_norm - chp_pt_norm, win_len)\n",
        "        sn_pt_norm_smth = boxcar_center(sn_pt_norm, win_len)\n",
        "        sn_pt_ang = np.zeros(dataRange - 1)\n",
        "        sn_disp = np.zeros(dataRange - 1)\n",
        "        pt_disp = np.zeros(dataRange - 1)\n",
        "        for k in range(0, dataRange - 1):\n",
        "            b_3d = np.hstack([sn_pt[k + 1, :], 0])\n",
        "            a_3d = np.hstack([sn_pt[k, :], 0])\n",
        "            c = np.cross(b_3d, a_3d)\n",
        "            sn_pt_ang[k] = np.dot(np.dot(np.sign(c[2]), 180) / np.pi,\n",
        "                                  math.atan2(np.linalg.norm(c), np.dot(sn_pt[k, :], sn_pt[k + 1, :])))\n",
        "            sn_disp[k] = np.linalg.norm(\n",
        "                data[m][k + 1, 2 * bodyparts.get('Snout/Head'):2 * bodyparts.get('Snout/Head') + 1] -\n",
        "                data[m][k, 2 * bodyparts.get('Snout/Head'):2 * bodyparts.get('Snout/Head') + 1])\n",
        "            pt_disp[k] = np.linalg.norm(\n",
        "                data[m][k + 1, 2 * bodyparts.get('Tailbase'):2 * bodyparts.get('Tailbase') + 1] -\n",
        "                data[m][k, 2 * bodyparts.get('Tailbase'):2 * bodyparts.get('Tailbase') + 1])\n",
        "        sn_pt_ang_smth = boxcar_center(sn_pt_ang, win_len)\n",
        "        sn_disp_smth = boxcar_center(sn_disp, win_len)\n",
        "        pt_disp_smth = boxcar_center(pt_disp, win_len)\n",
        "        feats.append(np.vstack((sn_cfp_norm_smth[1:], sn_chp_norm_smth[1:], fpd_norm_smth[1:],\n",
        "                                sn_pt_norm_smth[1:], sn_pt_ang_smth[:], sn_disp_smth[:], pt_disp_smth[:])))\n",
        "    logging.info('Done extracting features from a total of {} training CSV files.'.format(len(data)))\n",
        "    if comp == 0:\n",
        "        f_10fps = []\n",
        "        f_10fps_sc = []\n",
        "        trained_tsne = []\n",
        "    for n in range(0, len(feats)):\n",
        "        feats1 = np.zeros(len(data[n]))\n",
        "        for k in range(round(fps / 10) - 1, len(feats[n][0]), round(fps / 10)):\n",
        "            if k > round(fps / 10) - 1:\n",
        "                feats1 = np.concatenate((feats1.reshape(feats1.shape[0], feats1.shape[1]),\n",
        "                                         np.hstack((np.mean((feats[n][0:4, range(k - round(fps / 10), k)]), axis=1),\n",
        "                                                    np.sum((feats[n][4:7, range(k - round(fps / 10), k)]),\n",
        "                                                           axis=1))).reshape(len(feats[0]), 1)), axis=1)\n",
        "            else:\n",
        "                feats1 = np.hstack((np.mean((feats[n][0:4, range(k - round(fps / 10), k)]), axis=1),\n",
        "                                    np.sum((feats[n][4:7, range(k - round(fps / 10), k)]), axis=1))).reshape(\n",
        "                    len(feats[0]), 1)\n",
        "        logging.info('Done integrating features into 100ms bins from CSV file {}.'.format(n + 1))\n",
        "        if comp == 1:\n",
        "            if n > 0:\n",
        "                f_10fps = np.concatenate((f_10fps, feats1), axis=1)\n",
        "            else:\n",
        "                f_10fps = feats1\n",
        "        else:\n",
        "            f_10fps.append(feats1)\n",
        "            scaler = StandardScaler()\n",
        "            scaler.fit(feats1.T)\n",
        "            feats1_stnd = scaler.transform(feats1.T).T\n",
        "            f_10fps_sc.append(feats1_stnd)\n",
        "            if f_10fps_sc[n].shape[1] < 10000:\n",
        "                print(\"Insufficient data, exiting...\")\n",
        "                exit()\n",
        "            np.random.seed(23)  # For reproducibility\n",
        "            logging.info('Training t-SNE to embed {} instances from {} D '\n",
        "                         'into 3 D from CSV file {}...'.format(f_10fps_sc[n].shape[1], f_10fps_sc[n].shape[0],\n",
        "                                                               n + 1))\n",
        "            trained_tsne_i = tsne(f_10fps_sc[n].T, dimensions=3, perplexity=np.sqrt(f_10fps_sc[n].shape[1]),\n",
        "                                  theta=0.5, rand_seed=23)\n",
        "            trained_tsne.append(trained_tsne_i)\n",
        "            logging.info('Done embedding into 3 D.')\n",
        "    if comp == 1:\n",
        "        if f_10fps.shape[1] < 10000:\n",
        "            print(\"Insufficient data, exiting...\")\n",
        "            exit()\n",
        "        scaler = StandardScaler()\n",
        "        scaler.fit(f_10fps.T)\n",
        "        f_10fps_sc = scaler.transform(f_10fps.T).T\n",
        "        np.random.seed(23)  # For reproducibility\n",
        "        logging.info('Training t-SNE to embed {} instances from {} D '\n",
        "                     'into 3 D from a total of {} CSV files...'.format(f_10fps_sc.shape[1], f_10fps_sc.shape[0],\n",
        "                                                                       len(data)))\n",
        "        trained_tsne = tsne(f_10fps_sc.T, dimensions=3, perplexity=np.sqrt(f_10fps_sc.shape[1]),\n",
        "                            theta=0.5, rand_seed=23)\n",
        "        logging.info('Done embedding into 3 D.')\n",
        "    return f_10fps, f_10fps_sc, trained_tsne, scaler\n",
        "\n",
        "\n",
        "def bsoid_gmm(trained_tsne, comp=COMP, emgmm_params=EMGMM_PARAMS):\n",
        "    \"\"\"\n",
        "    Trains EM-GMM (unsupervised) given learned t-SNE space\n",
        "    :param trained_tsne: 2D array, trained t-SNE space\n",
        "    :param comp: boolean (0 or 1), argument to compile data or not in LOCAL_CONFIG\n",
        "    :param emgmm_params: dict, EMGMM_PARAMS in GLOBAL_CONFIG\n",
        "    :return assignments: Converged EM-GMM group assignments\n",
        "    \"\"\"\n",
        "    if comp == 1:\n",
        "        logging.info('Running EM-GMM on {} instances in {} D space...'.format(*trained_tsne.shape))\n",
        "        gmm = mixture.GaussianMixture(**emgmm_params).fit(trained_tsne)\n",
        "        logging.info('Predicting labels for {} instances in {} D space...'.format(*trained_tsne.shape))\n",
        "        assigns = gmm.predict(trained_tsne)\n",
        "    else:\n",
        "        assigns = []\n",
        "        for i in tqdm(range(len(trained_tsne))):\n",
        "            logging.info('Running EM-GMM on {} instances in {} D space...'.format(*trained_tsne[i].shape))\n",
        "            gmm = mixture.GaussianMixture(**emgmm_params).fit(trained_tsne[i])\n",
        "            logging.info('Predicting labels for {} instances in {} D space...'.format(*trained_tsne[i].shape))\n",
        "            assign = gmm.predict(trained_tsne[i])\n",
        "            assigns.append(assign)\n",
        "    logging.info('Done predicting labels for {} instances in {} D space...'.format(*trained_tsne.shape))\n",
        "    uk = list(np.unique(assigns))\n",
        "    assignments_li = []\n",
        "    for i in assigns:\n",
        "        indexVal = uk.index(i)\n",
        "        assignments_li.append(indexVal)\n",
        "    assignments = np.array(assignments_li)\n",
        "    return assignments\n",
        "\n",
        "\n",
        "def bsoid_svm(feats, labels, comp=COMP, hldout=HLDOUT, cv_it=CV_IT, svm_params=SVM_PARAMS):\n",
        "    \"\"\"\n",
        "    Trains multiclass SVM classifier\n",
        "    :param feats: 2D array, original feature space, standardized\n",
        "    :param labels: 1D array, GMM output assignments\n",
        "    :param hldout: scalar, test partition ratio for validating SVM performance in GLOBAL_CONFIG\n",
        "    :param cv_it: scalar, iterations for cross-validation in GLOBAL_CONFIG\n",
        "    :param svm_params: dict, SVM parameters in GLOBAL_CONFIG\n",
        "    :return classifier: obj, SVM.sklearn.svm._classes.SVC classifier\n",
        "    :return scores: 1D array, cross-validated accuracy\n",
        "    \"\"\"\n",
        "    if comp == 1:\n",
        "        feats_train, feats_test, labels_train, labels_test = train_test_split(feats.T, labels.T, test_size=hldout,\n",
        "                                                                              random_state=23)\n",
        "        logging.info(\n",
        "            'Training SVM classifier on randomly partitioned {}% of training data...'.format((1 - hldout) * 100))\n",
        "        classifier = svm.SVC(**svm_params)\n",
        "        classifier.fit(feats_train, labels_train)\n",
        "        logging.info('Done training SVM classifier mapping {} features to {} assignments.'.format(feats_train.shape,\n",
        "                                                                                                  labels_train.shape))\n",
        "        logging.info('Predicting randomly sampled (non-overlapped) assignments '\n",
        "                     'using the remaining {}%...'.format(HLDOUT * 100))\n",
        "        scores = cross_val_score(classifier, feats_test, labels_test, cv=cv_it, n_jobs=-1)\n",
        "        timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "        if PLOT_TRAINING:\n",
        "            np.set_printoptions(precision=2)\n",
        "            titles_options = [(\"Non-normalized confusion matrix\", None),\n",
        "                              (\"Normalized confusion matrix\", 'true')]\n",
        "            titlenames = [(\"counts\"), (\"norm\")]\n",
        "            j = 0\n",
        "            for title, normalize in titles_options:\n",
        "                disp = plot_confusion_matrix(classifier, feats_test, labels_test,\n",
        "                                             cmap=plt.cm.Blues,\n",
        "                                             normalize=normalize)\n",
        "                disp.ax_.set_title(title)\n",
        "                print(title)\n",
        "                print(disp.confusion_matrix)\n",
        "                my_file = 'confusion_matrix_{}_'.format(titlenames[j])\n",
        "                disp.figure_.savefig(os.path.join(OUTPUT_PATH, str.join('', (my_file, timestr, '.svg'))))\n",
        "                j += 1\n",
        "            plt.show()\n",
        "    else:\n",
        "        classifier = []\n",
        "        scores = []\n",
        "        for i in range(len(feats)):\n",
        "            feats_train, feats_test, labels_train, labels_test = train_test_split(feats[i].T, labels[i].T,\n",
        "                                                                                  test_size=hldout,\n",
        "                                                                                  random_state=23)\n",
        "            logging.info(\n",
        "                'Training SVM classifier on randomly partitioned {}% of training data...'.format((1 - hldout) * 100))\n",
        "            clf = svm.SVC(**svm_params)\n",
        "            clf.fit(feats_train, labels_train)\n",
        "            classifier.append(clf)\n",
        "            logging.info('Done training SVM classifier mapping {} features to {} assignments.'.format(feats_train.shape,\n",
        "                                                                                                      labels_train.shape))\n",
        "            logging.info('Predicting randomly sampled (non-overlapped) assignments '\n",
        "                         'using the remaining {}%...'.format(HLDOUT * 100))\n",
        "            sc = cross_val_score(classifier, feats_test, labels_test, cv=cv_it, n_jobs=-1)\n",
        "            timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "            if PLOT_TRAINING:\n",
        "                np.set_printoptions(precision=2)\n",
        "                titles_options = [(\"Non-normalized confusion matrix\", None),\n",
        "                                  (\"Normalized confusion matrix\", 'true')]\n",
        "                j = 0\n",
        "                titlenames = [(\"counts\"), (\"norm\")]\n",
        "                for title, normalize in titles_options:\n",
        "                    disp = plot_confusion_matrix(classifier, feats_test, labels_test,\n",
        "                                                 cmap=plt.cm.Blues,\n",
        "                                                 normalize=normalize)\n",
        "                    disp.ax_.set_title(title)\n",
        "                    print(title)\n",
        "                    print(disp.confusion_matrix)\n",
        "                    my_file = 'confusion_matrix_clf{}_{}_'.format(i, titlenames[j])\n",
        "                    disp.figure_.savefig(os.path.join(OUTPUT_PATH, str.join('', (my_file, timestr, '.svg'))))\n",
        "                    j += 1\n",
        "                plt.show()\n",
        "    logging.info('Done cross-validating the learned SVM classifier.'.format(feats_train.shape, labels_train.shape))\n",
        "    return classifier, scores\n",
        "\n",
        "\n",
        "def bsoid_train(train_folders):\n",
        "    \"\"\"\n",
        "    :param train_folders: list, training data folders\n",
        "    :return f_10fps: 2D array, features\n",
        "    :return trained_tsne: 2D array, trained t-SNE space\n",
        "    :return gmm_assignments: Converged EM-GMM group assignments\n",
        "    :return classifier: obj, SVM.sklearn.svm._classes.SVC classifier\n",
        "    :return scores: 1D array, cross-validated accuracy\n",
        "    \"\"\"\n",
        "    filenames, training_data, perc_rect = likelihoodprocessing(train_folders)\n",
        "    f_10fps, f_10fps_sc, trained_tsne, scaler = bsoid_tsne(training_data)\n",
        "    gmm_assignments = bsoid_gmm(trained_tsne)\n",
        "    classifier, scores = bsoid_svm(f_10fps_sc, gmm_assignments)\n",
        "    if PLOT_TRAINING:\n",
        "        plot_classes(trained_tsne, gmm_assignments)\n",
        "        plot_accuracy(scores)\n",
        "        plot_feats(f_10fps, gmm_assignments)\n",
        "    return f_10fps, trained_tsne, scaler, gmm_assignments, classifier, scores\n",
        "\n",
        "\n",
        "def bsoid_build(train_folders):\n",
        "    \"\"\"\n",
        "    :param train_folders: list, folders to build behavioral model on\n",
        "    :returns f_10fps, trained_tsne, gmm_assignments, classifier, scores: see bsoid_py.train\n",
        "    Automatically saves single CSV file containing training outputs (in 10Hz, 100ms per row):\n",
        "    1. original features (number of training data points by 7 dimensions, columns 1-7)\n",
        "    2. embedded features (number of training data points by 3 dimensions, columns 8-10)\n",
        "    3. em-gmm assignments (number of training data points by 1, columns 11)\n",
        "    Automatically saves classifier in OUTPUTPATH with MODELNAME in LOCAL_CONFIG\n",
        "    \"\"\"\n",
        "    f_10fps, trained_tsne, scaler, gmm_assignments, classifier, scores = bsoid_train(train_folders)\n",
        "    alldata = np.concatenate([f_10fps.T, trained_tsne, gmm_assignments.reshape(len(gmm_assignments), 1)], axis=1)\n",
        "    micolumns = pd.MultiIndex.from_tuples([('Features', 'Relative snout to forepaws placement'),\n",
        "                                           ('', 'Relative snout to hind paws placement'),\n",
        "                                           ('', 'Inter-forepaw distance'),\n",
        "                                           ('', 'Body length'), ('', 'Body angle'), ('', 'Snout displacement'),\n",
        "                                           ('', 'Tail-base displacement'),\n",
        "                                           ('Embedded t-SNE', 'Dimension 1'), ('', 'Dimension 2'),\n",
        "                                           ('', 'Dimension 3'), ('EM-GMM', 'Assignment No.')],\n",
        "                                          names=['Type', 'Frame@10Hz'])\n",
        "    training_data = pd.DataFrame(alldata, columns=micolumns)\n",
        "    timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "    training_data.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_train10Hz', timestr, '.csv')))),\n",
        "                         index=True, chunksize=10000, encoding='utf-8')\n",
        "    with open(os.path.join(OUTPUT_PATH, str.join('', ('bsoid_', MODEL_NAME, '.sav'))), 'wb') as f:\n",
        "        joblib.dump([classifier, scaler], f)\n",
        "    logging.info('Saved.')\n",
        "    return f_10fps, trained_tsne, scaler, gmm_assignments, classifier, scores\n",
        "\n",
        "\n",
        "def bsoid_extract(data, bodyparts=BODYPARTS, fps=FPS):\n",
        "    \"\"\"\n",
        "    Extracts features based on (x,y) positions\n",
        "    :param data: list, csv data\n",
        "    :param bodyparts: dict, body parts with their orders\n",
        "    :param fps: scalar, input for camera frame-rate\n",
        "    :return f_10fps: 2D array, extracted features\n",
        "    \"\"\"\n",
        "    win_len = np.int(np.round(0.05 / (1 / fps)) * 2 - 1)\n",
        "    feats = []\n",
        "    for m in range(len(data)):\n",
        "        logging.info('Extracting features from CSV file {}...'.format(m + 1))\n",
        "        dataRange = len(data[m])\n",
        "        fpd = data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1'):2 * bodyparts.get('Forepaw/Shoulder1') + 2] - \\\n",
        "              data[m][:, 2 * bodyparts.get('Forepaw/Shoulder2'):2 * bodyparts.get('Forepaw/Shoulder2') + 2]\n",
        "        cfp = np.vstack(((data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1')] +\n",
        "                          data[m][:, 2 * bodyparts.get('Forepaw/Shoulder2')]) / 2,\n",
        "                         (data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1') + 1] +\n",
        "                          data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1') + 1]) / 2)).T\n",
        "        cfp_pt = np.vstack(([cfp[:, 0] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                             cfp[:, 1] - data[m][:, 2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        chp = np.vstack((((data[m][:, 2 * bodyparts.get('Hindpaw/Hip1')] +\n",
        "                           data[m][:, 2 * bodyparts.get('Hindpaw/Hip2')]) / 2),\n",
        "                         ((data[m][:, 2 * bodyparts.get('Hindpaw/Hip1') + 1] +\n",
        "                           data[m][:, 2 * bodyparts.get('Hindpaw/Hip2') + 1]) / 2))).T\n",
        "        chp_pt = np.vstack(([chp[:, 0] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                             chp[:, 1] - data[m][:, 2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        sn_pt = np.vstack(([data[m][:, 2 * bodyparts.get('Snout/Head')] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                            data[m][:, 2 * bodyparts.get('Snout/Head') + 1] - data[m][:,\n",
        "                                                                         2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        fpd_norm = np.zeros(dataRange)\n",
        "        cfp_pt_norm = np.zeros(dataRange)\n",
        "        chp_pt_norm = np.zeros(dataRange)\n",
        "        sn_pt_norm = np.zeros(dataRange)\n",
        "        for i in range(1, dataRange):\n",
        "            fpd_norm[i] = np.array(np.linalg.norm(fpd[i, :]))\n",
        "            cfp_pt_norm[i] = np.linalg.norm(cfp_pt[i, :])\n",
        "            chp_pt_norm[i] = np.linalg.norm(chp_pt[i, :])\n",
        "            sn_pt_norm[i] = np.linalg.norm(sn_pt[i, :])\n",
        "        fpd_norm_smth = boxcar_center(fpd_norm, win_len)\n",
        "        sn_cfp_norm_smth = boxcar_center(sn_pt_norm - cfp_pt_norm, win_len)\n",
        "        sn_chp_norm_smth = boxcar_center(sn_pt_norm - chp_pt_norm, win_len)\n",
        "        sn_pt_norm_smth = boxcar_center(sn_pt_norm, win_len)\n",
        "        sn_pt_ang = np.zeros(dataRange - 1)\n",
        "        sn_disp = np.zeros(dataRange - 1)\n",
        "        pt_disp = np.zeros(dataRange - 1)\n",
        "        for k in range(0, dataRange - 1):\n",
        "            b_3d = np.hstack([sn_pt[k + 1, :], 0])\n",
        "            a_3d = np.hstack([sn_pt[k, :], 0])\n",
        "            c = np.cross(b_3d, a_3d)\n",
        "            sn_pt_ang[k] = np.dot(np.dot(np.sign(c[2]), 180) / np.pi,\n",
        "                                  math.atan2(np.linalg.norm(c), np.dot(sn_pt[k, :], sn_pt[k + 1, :])))\n",
        "            sn_disp[k] = np.linalg.norm(data[m][k + 1, 2 * bodyparts.get('Snout/Head'):2 * bodyparts.get('Snout/Head') + 1] -\n",
        "                                        data[m][k, 2 * bodyparts.get('Snout/Head'):2 * bodyparts.get('Snout/Head') + 1])\n",
        "            pt_disp[k] = np.linalg.norm(\n",
        "                data[m][k + 1, 2 * bodyparts.get('Tailbase'):2 * bodyparts.get('Tailbase') + 1] -\n",
        "                data[m][k, 2 * bodyparts.get('Tailbase'):2 * bodyparts.get('Tailbase') + 1])\n",
        "        sn_pt_ang_smth = boxcar_center(sn_pt_ang, win_len)\n",
        "        sn_disp_smth = boxcar_center(sn_disp, win_len)\n",
        "        pt_disp_smth = boxcar_center(pt_disp, win_len)\n",
        "        feats.append(np.vstack((sn_cfp_norm_smth[1:], sn_chp_norm_smth[1:], fpd_norm_smth[1:],\n",
        "                                sn_pt_norm_smth[1:], sn_pt_ang_smth[:], sn_disp_smth[:], pt_disp_smth[:])))\n",
        "    logging.info('Done extracting features from a total of {} training CSV files.'.format(len(data)))\n",
        "    f_10fps = []\n",
        "    for n in range(0, len(feats)):\n",
        "        feats1 = np.zeros(len(data[n]))\n",
        "        for k in range(round(fps / 10) - 1, len(feats[n][0]), round(fps / 10)):\n",
        "            if k > round(fps / 10) - 1:\n",
        "                feats1 = np.concatenate((feats1.reshape(feats1.shape[0], feats1.shape[1]),\n",
        "                                         np.hstack((np.mean((feats[n][0:4, range(k - round(fps / 10), k)]), axis=1),\n",
        "                                                    np.sum((feats[n][4:7, range(k - round(fps / 10), k)]),\n",
        "                                                           axis=1))).reshape(len(feats[0]), 1)), axis=1)\n",
        "            else:\n",
        "                feats1 = np.hstack((np.mean((feats[n][0:4, range(k - round(fps / 10), k)]), axis=1),\n",
        "                                    np.sum((feats[n][4:7, range(k - round(fps / 10), k)]), axis=1))).reshape(\n",
        "                    len(feats[0]), 1)\n",
        "        logging.info('Done integrating features into 100ms bins from CSV file {}.'.format(n + 1))\n",
        "        f_10fps.append(feats1)\n",
        "    return f_10fps\n",
        "\n",
        "\n",
        "def bsoid_extract(data, bodyparts=BODYPARTS, fps=FPS):\n",
        "    \"\"\"\n",
        "    Extracts features based on (x,y) positions\n",
        "    :param data: list, csv data\n",
        "    :param bodyparts: dict, body parts with their orders\n",
        "    :param fps: scalar, input for camera frame-rate\n",
        "    :return f_10fps: 2D array, extracted features\n",
        "    \"\"\"\n",
        "    win_len = np.int(np.round(0.05 / (1 / fps)) * 2 - 1)\n",
        "    feats = []\n",
        "    for m in range(len(data)):\n",
        "        logging.info('Extracting features from CSV file {}...'.format(m + 1))\n",
        "        dataRange = len(data[m])\n",
        "        fpd = data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1'):2 * bodyparts.get('Forepaw/Shoulder1') + 2] - \\\n",
        "              data[m][:, 2 * bodyparts.get('Forepaw/Shoulder2'):2 * bodyparts.get('Forepaw/Shoulder2') + 2]\n",
        "        cfp = np.vstack(((data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1')] +\n",
        "                          data[m][:, 2 * bodyparts.get('Forepaw/Shoulder2')]) / 2,\n",
        "                         (data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1') + 1] +\n",
        "                          data[m][:, 2 * bodyparts.get('Forepaw/Shoulder1') + 1]) / 2)).T\n",
        "        cfp_pt = np.vstack(([cfp[:, 0] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                             cfp[:, 1] - data[m][:, 2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        chp = np.vstack((((data[m][:, 2 * bodyparts.get('Hindpaw/Hip1')] +\n",
        "                           data[m][:, 2 * bodyparts.get('Hindpaw/Hip2')]) / 2),\n",
        "                         ((data[m][:, 2 * bodyparts.get('Hindpaw/Hip1') + 1] +\n",
        "                           data[m][:, 2 * bodyparts.get('Hindpaw/Hip2') + 1]) / 2))).T\n",
        "        chp_pt = np.vstack(([chp[:, 0] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                             chp[:, 1] - data[m][:, 2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        sn_pt = np.vstack(([data[m][:, 2 * bodyparts.get('Snout/Head')] - data[m][:, 2 * bodyparts.get('Tailbase')],\n",
        "                            data[m][:, 2 * bodyparts.get('Snout/Head') + 1] - data[m][:,\n",
        "                                                                              2 * bodyparts.get('Tailbase') + 1]])).T\n",
        "        fpd_norm = np.zeros(dataRange)\n",
        "        cfp_pt_norm = np.zeros(dataRange)\n",
        "        chp_pt_norm = np.zeros(dataRange)\n",
        "        sn_pt_norm = np.zeros(dataRange)\n",
        "        for i in range(1, dataRange):\n",
        "            fpd_norm[i] = np.array(np.linalg.norm(fpd[i, :]))\n",
        "            cfp_pt_norm[i] = np.linalg.norm(cfp_pt[i, :])\n",
        "            chp_pt_norm[i] = np.linalg.norm(chp_pt[i, :])\n",
        "            sn_pt_norm[i] = np.linalg.norm(sn_pt[i, :])\n",
        "        fpd_norm_smth = boxcar_center(fpd_norm, win_len)\n",
        "        sn_cfp_norm_smth = boxcar_center(sn_pt_norm - cfp_pt_norm, win_len)\n",
        "        sn_chp_norm_smth = boxcar_center(sn_pt_norm - chp_pt_norm, win_len)\n",
        "        sn_pt_norm_smth = boxcar_center(sn_pt_norm, win_len)\n",
        "        sn_pt_ang = np.zeros(dataRange - 1)\n",
        "        sn_disp = np.zeros(dataRange - 1)\n",
        "        pt_disp = np.zeros(dataRange - 1)\n",
        "        for k in range(0, dataRange - 1):\n",
        "            b_3d = np.hstack([sn_pt[k + 1, :], 0])\n",
        "            a_3d = np.hstack([sn_pt[k, :], 0])\n",
        "            c = np.cross(b_3d, a_3d)\n",
        "            sn_pt_ang[k] = np.dot(np.dot(np.sign(c[2]), 180) / np.pi,\n",
        "                                  math.atan2(np.linalg.norm(c), np.dot(sn_pt[k, :], sn_pt[k + 1, :])))\n",
        "            sn_disp[k] = np.linalg.norm(\n",
        "                data[m][k + 1, 2 * bodyparts.get('Snout/Head'):2 * bodyparts.get('Snout/Head') + 1] -\n",
        "                data[m][k, 2 * bodyparts.get('Snout/Head'):2 * bodyparts.get('Snout/Head') + 1])\n",
        "            pt_disp[k] = np.linalg.norm(\n",
        "                data[m][k + 1, 2 * bodyparts.get('Tailbase'):2 * bodyparts.get('Tailbase') + 1] -\n",
        "                data[m][k, 2 * bodyparts.get('Tailbase'):2 * bodyparts.get('Tailbase') + 1])\n",
        "        sn_pt_ang_smth = boxcar_center(sn_pt_ang, win_len)\n",
        "        sn_disp_smth = boxcar_center(sn_disp, win_len)\n",
        "        pt_disp_smth = boxcar_center(pt_disp, win_len)\n",
        "        feats.append(np.vstack((sn_cfp_norm_smth[1:], sn_chp_norm_smth[1:], fpd_norm_smth[1:],\n",
        "                                sn_pt_norm_smth[1:], sn_pt_ang_smth[:], sn_disp_smth[:], pt_disp_smth[:])))\n",
        "    logging.info('Done extracting features from a total of {} training CSV files.'.format(len(data)))\n",
        "    f_10fps = []\n",
        "    for n in range(0, len(feats)):\n",
        "        feats1 = np.zeros(len(data[n]))\n",
        "        for k in range(round(fps / 10) - 1, len(feats[n][0]), round(fps / 10)):\n",
        "            if k > round(fps / 10) - 1:\n",
        "                feats1 = np.concatenate((feats1.reshape(feats1.shape[0], feats1.shape[1]),\n",
        "                                         np.hstack((np.mean((feats[n][0:4, range(k - round(fps / 10), k)]), axis=1),\n",
        "                                                    np.sum((feats[n][4:7, range(k - round(fps / 10), k)]),\n",
        "                                                           axis=1))).reshape(len(feats[0]), 1)), axis=1)\n",
        "            else:\n",
        "                feats1 = np.hstack((np.mean((feats[n][0:4, range(k - round(fps / 10), k)]), axis=1),\n",
        "                                    np.sum((feats[n][4:7, range(k - round(fps / 10), k)]), axis=1))).reshape(\n",
        "                    len(feats[0]), 1)\n",
        "        logging.info('Done integrating features into 100ms bins from CSV file {}.'.format(n + 1))\n",
        "        f_10fps.append(feats1)\n",
        "    return f_10fps\n",
        "\n",
        "\n",
        "def bsoid_predict(feats, scaler, model):\n",
        "    \"\"\"\n",
        "    :param feats: list, multiple feats (original feature space)\n",
        "    :param model: Obj, SVM classifier\n",
        "    :return labels_fslow: list, label/100ms\n",
        "    \"\"\"\n",
        "    labels_fslow = []\n",
        "    for i in range(0, len(feats)):\n",
        "        logging.info('Predicting file {} with {} instances '\n",
        "                     'using learned classifier: {}{}...'.format(i + 1, feats[i].shape[1], 'bsoid_', MODEL_NAME))\n",
        "        feats_sc = scaler.transform(feats[i].T).T\n",
        "        labels = model.predict(feats_sc.T)\n",
        "        logging.info('Done predicting file {} with {} instances in {} D space.'.format(i + 1, feats[i].shape[1],\n",
        "                                                                                       feats[i].shape[0]))\n",
        "        labels_fslow.append(labels)\n",
        "    logging.info('Done predicting a total of {} files.'.format(len(feats)))\n",
        "    return labels_fslow\n",
        "\n",
        "\n",
        "def bsoid_frameshift(data_new, scaler, fps, model):\n",
        "    \"\"\"\n",
        "    Frame-shift paradigm to output behavior/frame\n",
        "    :param data_new: list, new data from predict_folders\n",
        "    :param fps: scalar, argument specifying camera frame-rate in LOCAL_CONFIG\n",
        "    :param model: Obj, SVM classifier\n",
        "    :return labels_fshigh, 1D array, label/frame\n",
        "    \"\"\"\n",
        "    labels_fs = []\n",
        "    labels_fs2 = []\n",
        "    labels_fshigh = []\n",
        "    for i in range(0, len(data_new)):\n",
        "        data_offset = []\n",
        "        for j in range(math.floor(fps / 10)):\n",
        "            data_offset.append(data_new[i][j:, :])\n",
        "        feats_new = bsoid_extract(data_offset)\n",
        "        labels = bsoid_predict(feats_new, scaler, model)\n",
        "        for m in range(0, len(labels)):\n",
        "            labels[m] = labels[m][::-1]\n",
        "        labels_pad = -1 * np.ones([len(labels), len(max(labels, key=lambda x: len(x)))])\n",
        "        for n, l in enumerate(labels):\n",
        "            labels_pad[n][0:len(l)] = l\n",
        "            labels_pad[n] = labels_pad[n][::-1]\n",
        "            if n > 0:\n",
        "                labels_pad[n][0:n] = labels_pad[n - 1][0:n]\n",
        "        labels_fs.append(labels_pad.astype(int))\n",
        "    for k in range(0, len(labels_fs)):\n",
        "        labels_fs2 = []\n",
        "        for l in range(math.floor(fps / 10)):\n",
        "            labels_fs2.append(labels_fs[k][l])\n",
        "        labels_fshigh.append(np.array(labels_fs2).flatten('F'))\n",
        "    logging.info('Done frameshift-predicting a total of {} files.'.format(len(data_new)))\n",
        "    return labels_fshigh\n",
        "\n",
        "\n",
        "def bsoid_test(predict_folders, scaler, fps, behv_model):\n",
        "    \"\"\"\n",
        "    :param predict_folders: list, data folders\n",
        "    :param fps: scalar, camera frame-rate\n",
        "    :behv_model: object, svm classifier\n",
        "    :return data_new: list, csv data\n",
        "    :return feats_new: 2D array, extracted features\n",
        "    :return labels_fslow, 1D array, label/100ms\n",
        "    :return labels_fshigh, 1D array, label/frame\n",
        "    \"\"\"\n",
        "    filenames, data_new, perc_rect = likelihoodprocessing(predict_folders)\n",
        "    feats_new = bsoid_extract(data_new)\n",
        "    labels_fslow = bsoid_predict(feats_new, scaler, behv_model)\n",
        "    labels_fshigh = bsoid_frameshift(data_new, scaler, fps, behv_model)\n",
        "    if PLOT_TRAINING:\n",
        "        plot_feats(feats_new, labels_fslow)\n",
        "    if GEN_VIDEOS:\n",
        "        videoprocessing(VID_NAME, labels_fslow[ID], FPS, FRAME_DIR)\n",
        "    return data_new, feats_new, labels_fslow, labels_fshigh\n",
        "\n",
        "\n",
        "\n",
        "def bsoid_run(predict_folders):\n",
        "    \"\"\"\n",
        "    :param predict_folders: list, folders to run prediction using behavioral model\n",
        "    :returns labels_fslow, labels_fshigh: see bsoid_py.classify\n",
        "    Automatically loads classifier in OUTPUTPATH with MODELNAME in LOCAL_CONFIG\n",
        "    Automatically saves CSV files containing new outputs (1 in 10Hz, 1 in FPS, both with same format):\n",
        "    1. original features (number of training data points by 7 dimensions, columns 1-7)\n",
        "    2. SVM predicted labels (number of training data points by 1, columns 8)\n",
        "    \"\"\"\n",
        "    with open(os.path.join(OUTPUT_PATH, str.join('', ('bsoid_', MODEL_NAME, '.sav'))), 'rb') as fr:\n",
        "        behv_model, scaler = joblib.load(fr)\n",
        "    data_new, feats_new, labels_fslow, labels_fshigh = bsoid_test(predict_folders, scaler, FPS, behv_model)\n",
        "    filenames = []\n",
        "    all_df = []\n",
        "    for i, fd in enumerate(predict_folders):  # Loop through folders\n",
        "        f = get_filenames(fd)\n",
        "        for j, filename in enumerate(f):\n",
        "            logging.info('Importing CSV file {} from folder {}'.format(j + 1, i + 1))\n",
        "            curr_df = pd.read_csv(filename, low_memory=False)\n",
        "            filenames.append(filename)\n",
        "            all_df.append(curr_df)\n",
        "    for i in range(0, len(feats_new)):\n",
        "        alldata = np.concatenate([feats_new[i].T, labels_fslow[i].reshape(len(labels_fslow[i]), 1)], axis=1)\n",
        "        micolumns = pd.MultiIndex.from_tuples([('Features', 'Relative snout to forepaws placement'),\n",
        "                                               ('', 'Relative snout to hind paws placement'),\n",
        "                                               ('', 'Inter-forepaw distance'),\n",
        "                                               ('', 'Body length'), ('', 'Body angle'), ('', 'Snout displacement'),\n",
        "                                               ('', 'Tail-base displacement'),\n",
        "                                               ('SVM classifier', 'B-SOiD labels')],\n",
        "                                              names=['Type', 'Frame@10Hz'])\n",
        "        predictions = pd.DataFrame(alldata, columns=micolumns)\n",
        "        timestr = time.strftime(\"_%Y%m%d_%H%M\")\n",
        "        csvname = os.path.basename(filenames[i]).rpartition('.')[0]\n",
        "        predictions.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_labels_10Hz', timestr, csvname,  '.csv')))),\n",
        "                           index=True, chunksize=10000, encoding='utf-8')\n",
        "        runlen_df1, dur_stats1, df_tm1 = bsoid_stats(labels_fslow[i])\n",
        "        if PLOT_TRAINING:\n",
        "            plot_tmat(df_tm1, FPS)\n",
        "        runlen_df1.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_runlen_10Hz', timestr, csvname, '.csv')))),\n",
        "                         index=True, chunksize=10000, encoding='utf-8')\n",
        "        dur_stats1.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_stats_10Hz', timestr, csvname, '.csv')))),\n",
        "                          index=True, chunksize=10000, encoding='utf-8')\n",
        "        df_tm1.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_transitions_10Hz', timestr, csvname, '.csv')))),\n",
        "                      index=True, chunksize=10000, encoding='utf-8')\n",
        "        labels_fshigh_pad = np.pad(labels_fshigh[i], (6, 0), 'edge')\n",
        "        df2 = pd.DataFrame(labels_fshigh_pad, columns={'B-SOiD labels'})\n",
        "        df2.loc[len(df2)] = ''\n",
        "        df2.loc[len(df2)] = ''\n",
        "        df2 = df2.shift()\n",
        "        df2.loc[0] = ''\n",
        "        df2 = df2.shift()\n",
        "        df2.loc[0] = ''\n",
        "        frames = [df2, all_df[0]]\n",
        "        xyfs_df = pd.concat(frames, axis=1)\n",
        "        csvname = os.path.basename(filenames[i]).rpartition('.')[0]\n",
        "        xyfs_df.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_labels_', str(FPS), 'Hz', timestr, csvname,\n",
        "                                                                '.csv')))),\n",
        "                       index=True, chunksize=10000, encoding='utf-8')\n",
        "        runlen_df2, dur_stats2, df_tm2 = bsoid_stats(labels_fshigh[i])\n",
        "        runlen_df2.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_runlen_', str(FPS), 'Hz', timestr, csvname,\n",
        "                                                                   '.csv')))),\n",
        "                         index=True, chunksize=10000, encoding='utf-8')\n",
        "        dur_stats2.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_stats_', str(FPS), 'Hz', timestr, csvname,\n",
        "                                                                   '.csv')))),\n",
        "                          index=True, chunksize=10000, encoding='utf-8')\n",
        "        df_tm2.to_csv((os.path.join(OUTPUT_PATH, str.join('', ('bsoid_transitions_', str(FPS), 'Hz', timestr, csvname,\n",
        "                                                               '.csv')))),\n",
        "                      index=True, chunksize=10000, encoding='utf-8')\n",
        "    with open(os.path.join(OUTPUT_PATH, str.join('', ('bsoid_predictions.sav'))), 'wb') as f:\n",
        "        joblib.dump([labels_fslow, labels_fshigh], f)\n",
        "    logging.info('All saved.')\n",
        "    return data_new, feats_new, labels_fslow, labels_fshigh\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPFOpn0gsyOL",
        "colab_type": "text"
      },
      "source": [
        "# Build your own machine learning behavioral model based on spatiotemporal patterning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8O_NaoTlot5L",
        "colab_type": "code",
        "outputId": "cd0dcfbb-e6c1-4bbe-e0da-f551b9222e81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "f_10fps, trained_tsne, scaler, gmm_assignments, classifier, scores = bsoid_build(TRAIN_FOLDERS)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:07:23 INFO     Importing CSV file 1 from folder 1\n",
            "2020-05-04 15:07:24 INFO     Extracting likelihood value...\n",
            "2020-05-04 15:07:24 INFO     Computing data threshold to forward fill any sub-threshold (x,y)...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 6/6 [00:07<00:00,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:07:32 INFO     Done preprocessing (x,y) from file 1, folder 1.\n",
            "2020-05-04 15:07:32 INFO     Importing CSV file 2 from folder 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:07:34 INFO     Extracting likelihood value...\n",
            "2020-05-04 15:07:34 INFO     Computing data threshold to forward fill any sub-threshold (x,y)...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 6/6 [00:08<00:00,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:07:42 INFO     Done preprocessing (x,y) from file 2, folder 1.\n",
            "2020-05-04 15:07:42 INFO     Processed 2 CSV files from folder: 041919\n",
            "2020-05-04 15:07:42 INFO     Processed a total of 2 CSV files, and compiled into a (2, 107999, 12) data list.\n",
            "2020-05-04 15:07:42 INFO     Extracting features from CSV file 1...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:07:55 INFO     Extracting features from CSV file 2...\n",
            "2020-05-04 15:08:08 INFO     Done extracting features from a total of 2 training CSV files.\n",
            "2020-05-04 15:08:10 INFO     Done integrating features into 100ms bins from CSV file 1.\n",
            "2020-05-04 15:08:13 INFO     Done integrating features into 100ms bins from CSV file 2.\n",
            "2020-05-04 15:08:13 INFO     Training t-SNE to embed 35998 instances from 7 D into 3 D from a total of 2 CSV files...\n",
            "2020-05-04 15:25:11 INFO     Done embedding into 3 D.\n",
            "2020-05-04 15:25:11 INFO     Running EM-GMM on 35998 instances in 3 D space...\n",
            "Initialization 0\n",
            "Initialization converged: True\n",
            "Initialization 1\n",
            "Initialization converged: True\n",
            "Initialization 2\n",
            "Initialization converged: True\n",
            "Initialization 3\n",
            "Initialization converged: True\n",
            "Initialization 4\n",
            "Initialization converged: True\n",
            "Initialization 5\n",
            "Initialization converged: True\n",
            "Initialization 6\n",
            "Initialization converged: True\n",
            "Initialization 7\n",
            "Initialization converged: True\n",
            "Initialization 8\n",
            "Initialization converged: True\n",
            "Initialization 9\n",
            "Initialization converged: True\n",
            "2020-05-04 15:25:16 INFO     Predicting labels for 35998 instances in 3 D space...\n",
            "2020-05-04 15:25:16 INFO     Done predicting labels for 35998 instances in 3 D space...\n",
            "2020-05-04 15:25:16 INFO     Training SVM classifier on randomly partitioned 80.0% of training data...\n",
            "2020-05-04 15:25:50 INFO     Done training SVM classifier mapping (28798, 7) features to (28798,) assignments.\n",
            "2020-05-04 15:25:50 INFO     Predicting randomly sampled (non-overlapped) assignments using the remaining 20.0%...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:667: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=10.\n",
            "  % (min_groups, self.n_splits)), UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Non-normalized confusion matrix\n",
            "[[ 109    0    0    0    0    4    1    0    0    0    9    0]\n",
            " [   0  299    0    0    8   11    0    9    3    0    0    0]\n",
            " [   1    0   10    0    0    0    3    0    0    0    0    0]\n",
            " [   0    1    0  397   10   22    0    0    0    0    0    0]\n",
            " [   0    9    0    3  708   13    3    1   17    5    0    0]\n",
            " [   1    5    0   19    8 2421    0   38    1    7    7    2]\n",
            " [   1    3    0    0    5    1  235    0    2    0    0    0]\n",
            " [   0   11    0    0    1   76    1  654    1    0    0    0]\n",
            " [   0    9    0    0   14    0    9    0  962    0    0    0]\n",
            " [   2    0    0    0    6    8    0    0    0   50    0    0]\n",
            " [   3    0    1    0    0    7    3    2    0    0  964    0]\n",
            " [   0    0    0    0    0    1    0    0    0    0    0    3]]\n",
            "Normalized confusion matrix\n",
            "[[8.86e-01 0.00e+00 0.00e+00 0.00e+00 0.00e+00 3.25e-02 8.13e-03 0.00e+00\n",
            "  0.00e+00 0.00e+00 7.32e-02 0.00e+00]\n",
            " [0.00e+00 9.06e-01 0.00e+00 0.00e+00 2.42e-02 3.33e-02 0.00e+00 2.73e-02\n",
            "  9.09e-03 0.00e+00 0.00e+00 0.00e+00]\n",
            " [7.14e-02 0.00e+00 7.14e-01 0.00e+00 0.00e+00 0.00e+00 2.14e-01 0.00e+00\n",
            "  0.00e+00 0.00e+00 0.00e+00 0.00e+00]\n",
            " [0.00e+00 2.33e-03 0.00e+00 9.23e-01 2.33e-02 5.12e-02 0.00e+00 0.00e+00\n",
            "  0.00e+00 0.00e+00 0.00e+00 0.00e+00]\n",
            " [0.00e+00 1.19e-02 0.00e+00 3.95e-03 9.33e-01 1.71e-02 3.95e-03 1.32e-03\n",
            "  2.24e-02 6.59e-03 0.00e+00 0.00e+00]\n",
            " [3.99e-04 1.99e-03 0.00e+00 7.57e-03 3.19e-03 9.65e-01 0.00e+00 1.51e-02\n",
            "  3.99e-04 2.79e-03 2.79e-03 7.97e-04]\n",
            " [4.05e-03 1.21e-02 0.00e+00 0.00e+00 2.02e-02 4.05e-03 9.51e-01 0.00e+00\n",
            "  8.10e-03 0.00e+00 0.00e+00 0.00e+00]\n",
            " [0.00e+00 1.48e-02 0.00e+00 0.00e+00 1.34e-03 1.02e-01 1.34e-03 8.79e-01\n",
            "  1.34e-03 0.00e+00 0.00e+00 0.00e+00]\n",
            " [0.00e+00 9.05e-03 0.00e+00 0.00e+00 1.41e-02 0.00e+00 9.05e-03 0.00e+00\n",
            "  9.68e-01 0.00e+00 0.00e+00 0.00e+00]\n",
            " [3.03e-02 0.00e+00 0.00e+00 0.00e+00 9.09e-02 1.21e-01 0.00e+00 0.00e+00\n",
            "  0.00e+00 7.58e-01 0.00e+00 0.00e+00]\n",
            " [3.06e-03 0.00e+00 1.02e-03 0.00e+00 0.00e+00 7.14e-03 3.06e-03 2.04e-03\n",
            "  0.00e+00 0.00e+00 9.84e-01 0.00e+00]\n",
            " [0.00e+00 0.00e+00 0.00e+00 0.00e+00 0.00e+00 2.50e-01 0.00e+00 0.00e+00\n",
            "  0.00e+00 0.00e+00 0.00e+00 7.50e-01]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:26:19 INFO     Done cross-validating the learned SVM classifier.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:26:53 INFO     Saved.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0pB0iP_6vo89",
        "colab_type": "text"
      },
      "source": [
        "# Predict new datasets with high consistency using your own model!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYMZ5hvQpEl6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b22bd06f-5078-47b8-88d1-b59d9f7901b3"
      },
      "source": [
        "data_new, feats_new, labels_fslow, labels_fshigh = bsoid_run(PREDICT_FOLDERS)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:57:59 INFO     Importing CSV file 1 from folder 1\n",
            "2020-05-04 15:58:00 INFO     Extracting likelihood value...\n",
            "2020-05-04 15:58:01 INFO     Computing data threshold to forward fill any sub-threshold (x,y)...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 6/6 [00:07<00:00,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:58:09 INFO     Done preprocessing (x,y) from file 1, folder 1.\n",
            "2020-05-04 15:58:09 INFO     Importing CSV file 2 from folder 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:58:10 INFO     Extracting likelihood value...\n",
            "2020-05-04 15:58:10 INFO     Computing data threshold to forward fill any sub-threshold (x,y)...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 6/6 [00:08<00:00,  1.37s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:58:18 INFO     Done preprocessing (x,y) from file 2, folder 1.\n",
            "2020-05-04 15:58:18 INFO     Processed 2 CSV files from folder: 041919\n",
            "2020-05-04 15:58:18 INFO     Processed a total of 2 CSV files, and compiled into a (2, 107999, 12) data list.\n",
            "2020-05-04 15:58:18 INFO     Extracting features from CSV file 1...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 15:58:31 INFO     Extracting features from CSV file 2...\n",
            "2020-05-04 15:58:44 INFO     Done extracting features from a total of 2 training CSV files.\n",
            "2020-05-04 15:58:47 INFO     Done integrating features into 100ms bins from CSV file 1.\n",
            "2020-05-04 15:58:49 INFO     Done integrating features into 100ms bins from CSV file 2.\n",
            "2020-05-04 15:58:49 INFO     Predicting file 1 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 15:58:55 INFO     Done predicting file 1 with 17999 instances in 7 D space.\n",
            "2020-05-04 15:58:55 INFO     Predicting file 2 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 15:59:00 INFO     Done predicting file 2 with 17999 instances in 7 D space.\n",
            "2020-05-04 15:59:00 INFO     Done predicting a total of 2 files.\n",
            "2020-05-04 15:59:00 INFO     Extracting features from CSV file 1...\n",
            "2020-05-04 15:59:13 INFO     Extracting features from CSV file 2...\n",
            "2020-05-04 15:59:25 INFO     Extracting features from CSV file 3...\n",
            "2020-05-04 15:59:38 INFO     Extracting features from CSV file 4...\n",
            "2020-05-04 15:59:51 INFO     Extracting features from CSV file 5...\n",
            "2020-05-04 16:00:04 INFO     Extracting features from CSV file 6...\n",
            "2020-05-04 16:00:16 INFO     Done extracting features from a total of 6 training CSV files.\n",
            "2020-05-04 16:00:19 INFO     Done integrating features into 100ms bins from CSV file 1.\n",
            "2020-05-04 16:00:22 INFO     Done integrating features into 100ms bins from CSV file 2.\n",
            "2020-05-04 16:00:24 INFO     Done integrating features into 100ms bins from CSV file 3.\n",
            "2020-05-04 16:00:27 INFO     Done integrating features into 100ms bins from CSV file 4.\n",
            "2020-05-04 16:00:29 INFO     Done integrating features into 100ms bins from CSV file 5.\n",
            "2020-05-04 16:00:32 INFO     Done integrating features into 100ms bins from CSV file 6.\n",
            "2020-05-04 16:00:32 INFO     Predicting file 1 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:00:37 INFO     Done predicting file 1 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:00:37 INFO     Predicting file 2 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:00:42 INFO     Done predicting file 2 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:00:42 INFO     Predicting file 3 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:00:48 INFO     Done predicting file 3 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:00:48 INFO     Predicting file 4 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:00:53 INFO     Done predicting file 4 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:00:53 INFO     Predicting file 5 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:00:58 INFO     Done predicting file 5 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:00:58 INFO     Predicting file 6 with 17998 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:01:03 INFO     Done predicting file 6 with 17998 instances in 7 D space.\n",
            "2020-05-04 16:01:03 INFO     Done predicting a total of 6 files.\n",
            "2020-05-04 16:01:03 INFO     Extracting features from CSV file 1...\n",
            "2020-05-04 16:01:16 INFO     Extracting features from CSV file 2...\n",
            "2020-05-04 16:01:29 INFO     Extracting features from CSV file 3...\n",
            "2020-05-04 16:01:41 INFO     Extracting features from CSV file 4...\n",
            "2020-05-04 16:01:54 INFO     Extracting features from CSV file 5...\n",
            "2020-05-04 16:02:07 INFO     Extracting features from CSV file 6...\n",
            "2020-05-04 16:02:20 INFO     Done extracting features from a total of 6 training CSV files.\n",
            "2020-05-04 16:02:22 INFO     Done integrating features into 100ms bins from CSV file 1.\n",
            "2020-05-04 16:02:25 INFO     Done integrating features into 100ms bins from CSV file 2.\n",
            "2020-05-04 16:02:27 INFO     Done integrating features into 100ms bins from CSV file 3.\n",
            "2020-05-04 16:02:30 INFO     Done integrating features into 100ms bins from CSV file 4.\n",
            "2020-05-04 16:02:33 INFO     Done integrating features into 100ms bins from CSV file 5.\n",
            "2020-05-04 16:02:35 INFO     Done integrating features into 100ms bins from CSV file 6.\n",
            "2020-05-04 16:02:35 INFO     Predicting file 1 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:02:40 INFO     Done predicting file 1 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:02:40 INFO     Predicting file 2 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:02:46 INFO     Done predicting file 2 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:02:46 INFO     Predicting file 3 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:02:51 INFO     Done predicting file 3 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:02:51 INFO     Predicting file 4 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:02:56 INFO     Done predicting file 4 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:02:56 INFO     Predicting file 5 with 17999 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:03:01 INFO     Done predicting file 5 with 17999 instances in 7 D space.\n",
            "2020-05-04 16:03:01 INFO     Predicting file 6 with 17998 instances using learned classifier: bsoid_c57bl6_n2_120min...\n",
            "2020-05-04 16:03:06 INFO     Done predicting file 6 with 17998 instances in 7 D space.\n",
            "2020-05-04 16:03:06 INFO     Done predicting a total of 6 files.\n",
            "2020-05-04 16:03:06 INFO     Done frameshift-predicting a total of 2 files.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rV65EdHQ0EhMTsXTpUtTW1mLKlCkAgJ49eyInJwdnz55FamoqnJyc0NTUhLS0NIcI9PwWQET2xOaKg0dHR1v8YWxrYqAnoragL3byzVgiIgdn0Vk3pB+HfIioLfCOnojIwfGO3sLs4TkCEbUvFklqplQqMXXqVMjlcgwfPhyXL1+WlqWnp0MulyMiIgKHDx+2WMOJiMg4Bu/oVSoV3njjDY2kZomJiRrTJDMzM+Ht7Y0LFy5AoVBg+fLlyMrKQmlpKRQKBUpKSlBZWYmxY8eirKwMzs7OOo9XfbKsXd4Vc/yeiKzFIknNsrOzsWrVKgBAUlIS5s+fDyEEsrOzMW3aNLi6uqJXr16Qy+UoLCxEbGysdXrjoHgRICJzWCSpmfo6Li4ueOqpp3Dr1i1UVFQgJiZGY9uKiooWx1DPdVPduQkb+94zrTft0MZWThlBRLZJfcj852ziYax6rhsiIrIsgw9j9SU1a35Ie/HiRaxduxYA0NjYiHv37sHX1xeXLl3C4sWLpcpT58+f15oQjYiIrMfgHb16UrOgoCAoFArs3btX4yHtf//3f2PNmjV488038f333yM+Ph4ymQxJSUm4evUqvv32W7z//vv405/+hGHDhuk9HguPEBE9ObMKj+hKajZ79mx4enoiLCwMqamp2Lp1K0aNGoXw8HAoFAoAwGuvvYbKykr0798fjY2N6Nmzp94ZN4D+wiNF0abVnI0uyjFpOyIie2F24ZGEhAQkJCS0+MzJ6fHIj5ubG5YtW4YTJ04gIyNDY70VK1ZgxYoVmD9/Prp166Z1/yw8QkRkPa3yMFZb5Sl1bVl4hIjI0Zkc6M2tPGUr9A0HcciHiByByYFe10Nadc2Vp/Ly8jQqT9kLXRcBXgCIyJ6YHOjNqTzV2kx9iEtE5AjsqsKUrQRs3tETka3RFztt4s1Ye8NpnkRkTxjoWxEf/BJRW7C5QP/w7AWbGaIhInIEZgX6vLw8LFiwACqVCnPmzEFaWprGcqVSiVmzZuHkyZPw9fVFVlYW0xvowLt9IrIWkwO9OQVJ6MmY8g3HHi4OvLgRtQ6TA705BUlkMpl5rSaD7H34iw+8iSzH5EBvTkESPz8/jfXUc91clTXidVSa2ixq75hCg9opFh4hImrHDBYe0cWYXDfq66gXJCEiotZj1B29ttk16rlu9u/fj9WrV6Nnz57Iz8/HJ598gpCQECQmJmLkyJEYNGgQ7t69i44dOxocn2fhESKiJ6ev8IjBFAgqlQp9+vTRmF2zb98+9O/fH5999hkWLlyI2tpazJ07F6tXr0ZCQgLu37+Pr776CnV1dfDw8EBoaCh8fHygUCikh7e66HuN1xp+mvmCzmUBu/JarR1EROYwKwWCvtk12gqSrF27FvPnzwfwuCCJm5sbLly4YG4fiIjIRAbH6LXNrqmoqNC5fmZmJsaPHy/9XFdXh+joaMTExODTTz/Vus3HH3+M6OhoREdHs8IUEZGFWXTWjbZKUleuXEFQUBAuXryI+Ph4DBw4EOHh4RrbscIUEZH1GAz05laSal43LCwMY8aMQXFxcYtAb284rk9E9sTg0I367Jr6+nooFAokJmq+tdhcSSonJ0ejktSdO3egVCoBANXV1Th+/LjGm7NERGR9Bu/ozakkdfbsWaSmpsLJyQlNTU1IS0tjoCciamV2VWHKVPqGWvTRNQzDoRsisjWsMNWKeBEgIlvDQK+Hqd8ETNkfLwJEZC1G5brJy8tDREQE5HI51q1b12K5UqnE1KlTIZfLMXz4cI0saunp6ZDL5YiIiMDhw4ct1nAiIjKOwTt6cwqMlJaWQqFQoKSkBJWVlRg7dizKysrg7Oxs1U7ZI0t/ezCVNb5ZmNI3fsMhshyzUiA001VgJDs7G9OmTYOrqyt69eoFuVyOwsJCxMbGWrwjthIo2ytb+f1b+sG7LdHVN31tb+3hQntoo623Qx9T/74NBnpzCoxUVFQgJiZGY1tt6RPUC4+cO3eOb8e2JVv53bd2O2yl36Ywte2t2Wd7aKM+ttIOPaxWeKQ5ffHly5exceNGpKenayw/e/Ysdu7cifXr1wMAPDw8NPLgNGPhESIi6zH4MFZXCoTmsfvc3Fw8++yzOHDgAEpLSzUKjHh7eyMyMhKnTp3CqVOn0LlzZ63pE4iIyHoM3tGrp0AICgqCQqHA3r17NcbuJ02ahJ07dyI7Oxu9evVCfHw8ZDIZBg8ejKNHj0KpVKKyshLnz5/HsGHD9B6PhUeIiJ6cvsIjJqdASEpKQlNTEwAgJSUFO3bswLp16xAREQGFQgHg8bcBpVIJT09PdOzYERs3bjQ44yY0NFTn2131f5msc7uOi/7LUFeIiByWvmebRs2jT0hIQFlZGcrLy7FixQoAj2ffhISEAHhcYOTNN9/EzJkzUVhYKM3QefHFF1FdXQ2lUon169dj+/btWvfPfPRERNZj1eLgvr6+UsriOXPm4OTJk1r3NW/ePBQVFaGoqAj+/v6mNomIiLQwOdAbk774xo0b0r9zcnLQr18/01tKREQmMXl6pTHpizds2ICcnBy4uLjAx8dH59ANERFZj12lKebDWCIi7ewqTbG4Wa43oOvCiwARkXYmj9ETEZF9YKAnInJwDPRERA7OIknNVCoV5syZg7S0NI3lSqUSs2bNwsmTJ+Hr64usrKw2SW/A8Xsias9MDvTmFCSxJaY8+OXFgYjsicmB3pyCJDKZzLxWtzFTLg6G8OJBRNZicqA3pyCJn5+fxnrqhUd+uNOAEXuumNos+7XH9gsbEJHtslrhEUth4REiIusxOdAHBQWhuLgYERERUKlUkMvlGD16tMY6nTp1wnPPPYfy8nLs2bNHKkhCREStx+QUCEqlEh4eHsjPz8eQIUPg7++PrKwsjcRma9aswenTp9GpUyf4+vqioqIC//mf/6l3vyw8QkT05PQVHjE50H/zzTeYP38+ampqoFKpEB4ejri4ODx69EhKalZXV4eZM2fi8OHDCAgIwJEjR6SHt7roy9dgD5qOLjRpO6cxH1m2IUTUrlgl101FRQUGDx6MrVu3AgB27dqFEydOICMjQ1rHzc0N+/fvR3JyMiZMmGAwyLcFUwIzgzIR2RObeBirPuvGGhWmTL3Lbq39mXM8XRcdU7YhIsdk1sNYQxWmjKU+60Zf3cPWDrCtydS+mbIdLwJE7YtVK0wREVHbMznQq1eY6tevH15++WWpwlROTg4A4Ntvv0VwcDD279+P1NRUREZGWqzhRERkHLuqMOXIQzf2gMM6RLbLripMke3i1FEi+8RAT1an6wLBCwBR67C9QF9zjUM07QRn/xC1jnZReITsDy8CRJbT7guPkP3hswKiJ8PCI9RuWHpIkBcOshc2V3jkXMUDDHvrK1ObRdSKWCyGbAcLjxARtWMmvxlrTK4b9XUaGxtZeISIqA2YfEevnusmKCgICoUCe/fu1VgnMTERO3bsQGxsLA4cOID4+HiD4/MsPEJE9OT0FR4xOdCr57pRqVSYPXu2lOumufBISkoKZs6cCblcDh8fHygUCoP7DQ0N1fkar6jM0Po5AMi6zze1K0REdk9f5l+jhm7y8vIQEREBuVyOdevWSZ8nJCSgrKwMb7zxBvbs2YNBgwbh+PHjePrppwE8Ljzyj3/8Ax4eHqivr8fChXwRioiotRm8ozdmvvzgwYNRVFQEd3d3bNq0CcuWLZPmy3fq1AmnTp2yXg+IiEgvg3f06vPlO3bsKM2XVxcXFwd3d3cAQExMDK5fv26d1hIR0RMzeEdvzHx5dZmZmRg/frz0c11dHaKjo+Hi4oK0tDRMmjSpxTaWKCXI8XsiIu0sOo9+9+7dKCoqQkFBgfTZlStXEBQUhIsXLyI+Ph4DBw5EeHi4xnbGlhIkIqInZzDQG1sbNj8/H2vXrkVBQQFcXV01tgeAsLAwjBkzBsXFxS0CvbXxbp+I2jODgd6Y+fLFxcVITU1FXl4eAgICpM/v3LkDd3d3uLq6orq6GsePH8eyZcv0H7DhJ72B2dJ4ESAiR2cw0BszX37p0qWora3FlClTAAA9e/ZETk4Ozp49i9TUVDg5OaGpqQlpaWkas3WIiMj6bK9m7NM98W2ugbv+NsY7fSKyNWbXjDWnwEh6ejoyMzPh7OyMDRs2YNy4ceb1xgaYOrTECwQRtQWLvDClq8BIaWkpFAoFSkpKUFlZibFjx6KsrAzOzs5W7ZSt4vMAImoLBgO9OQVGsrOzMW3aNLi6uqJXr16Qy+UoLCxEbGysdXpjx1rzATQvKkTti0VemNJVYKSiogIxMTEa21ZUVLQ4hkbhkfLbGJq43aTOkLG2t3UDiMjCzC48cv36dUREREClUuGZZ57RmEIJANXV1YiPj4ebmxv8/f3R0NAgLZs1axbWr18PALh9+7bGW7PNWHiEiMh6DOa66datG44dO4bc3FyUlpbiyy+/hIuL5vWhR48e2LJlC77//nu89NJLuHnzJnx9fREUFIQOHTrg1KlTOHXqFPr166f1ZSsiIrIeg3f0zbMv9RUMSU5ORlZWFuLi4lBTU4MuXbpAJpMhMTER77zzDpRKJSorK3H+/HkMGzZM7/FYeISI6MmZVXjk5s2bGDVqlPTC1MiRI9HY2KizwEhNTQ1mzZoFAIiMjAQAeHp6wsnJCW+99ZbWGTfqY/SdO3fWORcU+EJPS+MMdYWIyGGZXXikR48eKCsrQ3l5OSZOnAgAWLNmDRITEwE8LjCyf/9+rFq1Cr169UJ6erq07dWrV6FUKlFSUoJdu3ahvLy8xf7nzZuHoqIiFBUVwd/f/4k6R0RE+rWLpGb8JkBE7ZntJTUzmb5gTkTUfjGpGRGRg7O9pGZ6EvO0/l07h3WIyD6YndSs/dJ1YeEFgIjsh1GzboiIyH7xjt4knMVDRPaDgd7ieBEgItti1NBNXl4eIiIiIJfLsW7duhbLlUolpk6dCrlcjuHDh2tkUUtPT4dcLkdERAQOHz5sxNFq8DhYavuPiIieFAuPtKrWvFjx2wMRPcbCIw7LGhcVfRcPSx+PFyoiS7G9wiPnKhAdvdS03hARtVNmFx6xNhYeISKyHoMPY41Jaqa+TmNjI+7duycVHjEmIRoREVmPRZKaJSYmYseOHYiNjcWBAwcQHx8vFR6ZPn06Fi9ezMIjRERWZFbhEWOSmqkXHvHx8YFCoQDwuPDIyy+/jP79+8PFxQUbN240OOMmNDRUZ76GqrptOrfzd3vNUFeIiByWvsIjdpXUjIGeiEg7fbHT6rluZs+ejYCAAAwYMMDahyIiIi2sHuiTk5ORl5dn7cMQEZEOVg/0zz77LHx8fKx9GCIi0sEm5tGrvzBVVVXVxq0hInIsNpGPft68eSgqKkJRURH8/f3bujlERA7FJgI9ERFZDwM9EZGDs3qg//Wvf43Y2Fj88MMPCA4ORmZmprUPSUREaqwe6Pft24dt27YhLCwMbm5ufNhKRNTKrB7omwuX5ObmorS0FPv27UNpaam1D0tERP/H6oFevXBJx44dpcIlRETUOoyaR5+Xl4cFCxZApVJhzpw5SEtL01j+4YcfYuvWrXBxcYG/vz8++eQThISEAABGjhwJb29vREVFoWfPnpgyZUqLwiWahUfO6U3Oo9tGE7YhInIM+gqPGExqplKp0KdPH42asfv27dMoJfjFF19g+PDhcHd3x6ZNm3D06FFkZWUBANzc3DBjxgxs3boVALBr1y6cOHECGRkZFugaEREZYnDoxpihl7i4OLi7uwMAYmJicP369f8/gJMTi48QEbUhi9SMVZeZmYnx48dLP9fX1+Po0aMYPHgw3n77ba2FS34+dNO3b98n7ggRUXtmVuGRJ7F7924UFRWhoKBA+uzKlSv47rvv8MYbb2D69Ol48803ERkZqbGdes1YfTmVj1buMKldY7q/atJ2RET2Qt+zTYOB3ti6r/n5+Vi7di0KCgrg6uqqsX1QUBAuXbqE5ORkjBgx4knbT0REZjA4Rq9eM7a+vh4KhQKJiYka6xQXFyM1NRU5OTkICAiQPr9z5w6USiUAoLq6GsePH9d4iEtERNZnkZqxS5cuRW1tLaZMmQIA6NmzJ3JycnD27FmkpqbCyckJTU1NSEtLa5NAr2/Ih8M6RHc5nxgAAA6ASURBVOTo7KpmrKlj9KbiRYCI7EWb1owlIqK2ZRMVpmyVrm8QvNMnInvCQG8CjvkTkT2xuUBf03Cr1cfiiYgcmc0FenvHu30isjUWyV6pVCoxa9YsnDx5Er6+vsjKykJoaCgAID09HZmZmXB2dsaGDRswbtw4i3fCXrTmNxVeVIiomcFA31w4RD17ZWJiosZ8+MzMTHh7e+PChQtQKBRYvnw5srKyUFpaCoVCgZKSElRWVmLs2LEoKyuDs7OzVTtF1rmo8OJBZJ8MBnr17JUApOyV6oE+Ozsbq1atAgAkJSVh/vz5EEIgOzsb06ZNg6urK3r16gW5XI7CwkLExsZapzdkVbby7KQ1LzgciiNHYJHslerruLi44KmnnsKtW7dQUVGBmJgYjW0rKipaHEM9e2VFeRXeSvybab2hdsJW/j5spR1E+guP2MTDWPXslUREZFkG34w1Jnul+jqNjY24d+8efH19jc58SURE1mPwjn7o0KH47rvvEBYWBplMhocPHyI/P19jnfHjx2PGjBmQyWQAgOHDh0Mmk2HIkCF44YUXoFAo0NDQgOvXr2PYsGF6j+fn5yfN2CEiIuOYVXhEJpNBJpNBPfeZTCbTyF7ZHOABaKzXp08f+Pv7o7a2Fi4uLsjKyjI44yY0NFRnYh4iAEg/tVfnst9FTbf4dkT2wKzCI4WFhRg0aBAOHz4M4PG8+OzsbKxZs0Za57PPPsPu3bsRGxuLxsZGdOvWTQr4/v7+OHPmjLl9IDKKvmBu6f3xokL2wqqzbgDg0qVLGDx4MDw9PfHee+9h1KhRLY6hPuumqqrK9N6Q2VozQFk6KLc2U9tv6X7zwkGGWHXWTWBgIK5evQpfX1+cPHkSkyZNQklJCTw9PTXW+3nNWDKfNYKovQdmovbKIjVjm9cJDg7WmHUjk8mk+rFDhgxBeHg4ysrKGMzJ5tjzRczUtltj6EnXdvzW0baMmnXTXDM2KCgICoUCe/dqnszExETs2LEDsbGxOHDgAOLj4yGTyVBVVQUfHx84Ozvj4sWLOH/+vPSGLVmGPQcoalv822k/LFIzNiUlBTNnzoRcLoePjw8UCgUA4NixY1i5ciU6dOgAJycnbN68GT4+PlbvlD3iwzuyF6ZcIPj33baMGqNPSEhAQkKCxmfqs27c3Nywf//+FttNnjwZkydPNrOJRNReWWNYqj2yq+Lg9o5flYnsU2tPpTXlWYe+2GkTuW4cCYM5keOxxlRaUy4CpraDhUeIiNpAa94UtvvCI7wDJyJH5zCFR/hUn4hIO5srPHLu3DmLv1D1X/jQovsjIrI1LDxCRNSOGZUCobi4GBEREVCpVJDL5Rg9erTGOg0NDRg1ahS6dOkCPz8/3L59Wyo8MmvWLKxfvx4AUFlZiTlz5linJ0REpJXBefRKpRIeHh7Iz8/HkCFD4O/vj6ysLCQmJkrrLFiwALW1tcjMzERKSgqOHDmCa9euoaSkBIMGDcLDhw9RWVmJ5557DufPn9f7MLa1C49cvHtX57IwL69WawcRkTn0FR4xGOi/+eYbzJ8/HzU1NVCpVAgPD0dcXBwePXokpUCoq6vDzJkzUVxcDFdXV7i5ueHkyZMAAFdXVwQHB8PFxQUfffQRxo8fr7exrf3C1JR//kPnsv0v/arV2kFEZA6zXpiqqKjA4MGDsXXrVgDArl27cOLECWRkZEjrqKdAmD9/Prp16yYtU6lU8Pb2houLC5RKpdZjMB89EZH1WPRh7O7du1FUVISCggLpsytXriAoKAgXL15EfHw8Bg4ciPDwcI3t7C0fPb8FEJE9cTK0gjH56AEgPz8fa9euRU5OjpSDvnl7AAgLC8OYMWNQXFxsiXYTEZGRLJKPvri4GKmpqcjLy0NAQID0+Z07d+Du7g5XV1dUV1fj+PHjWLZsmeV7YSf4TYCI2oJF8tEvXboUtbW1mDJlCgCgZ8+eyMnJwdmzZ5GamgonJyc0NTUhLS1N441aR6QvmBMRtYV2kabYHoIv7+iJyBxMU2wHOKxDRNbiMIHeHu7aTcWLABGZw+CsGyIism9GBfq8vDxERERALpdj3bp1LZYrlUpMnToVcrkcw4cP18iilp6eDrlcjoiICBw+fNhiDSciIuPYVeERRx6eISKyFpsrPHLx7l0G9CfQmr8rfc8D+ByByHbZXOGR+h9/xKW1fzStN2RV0SaeF1O3IyLjsfAIEVE7ZpFcN+rrNDY24t69e1LhEWPy5BARkfVYJNdNYmIiduzYgdjYWBw4cADx8fGQyWRITEzE9OnTsXjxYlRWVuL8+fMYNmyY3uOZWnjkXOV9ncv6dvd84v0REdkTfYVHjMp1k5ycjIiICAghEBcX1yLXTW1tLfbu3Ytt27bB1dUVhw4dAgBERkbi9OnT8PT0hEwmQ1RUlMEZN6GhoTpf4x35ru7pmU/r2efx1eMMdZOIyK7pS/Fu1PTKHTt24Ny5c9L0ytLSUqxZs0ZaZ9iwYfjxxx/h7u6OTZs2ISMjA6NGjQIAuLu7o7a21gLdICIiUxgco1efXtmxY0dpeqW6uLg4uLu7AwBiYmJw/fp167SWiIiemEWmV6rLzMzUqAtbV1eH6OhouLi4IC0tDZMmTTKzyU9O35APh3WIyNHZRClB1owlIrIemyglOG/ePBQVFaGoqAj+/v4mdYSIiLRr96UEOaxDRI6OpQSJiByczZUS9OjeB0/P/VtbNwMA7+iJyH7oKyXIfPR6jHz3sNb/iIjsiV3lo7cVHNcnIntic/noHRkvEETUFmwiH70jMXVox9JDQrxwEFEzo16Yun79OiIiIqBSqfDMM89oTKEEgOrqasTHx8PNzQ3+/v5oaGiQls2aNQvr168HANy+fVvjrdlm6i9M4X4llIdWmNof+j/R/B0StStmFR7p1q0bjh07hjNnziA4OBghISGYOnWqxjo9evTA+++/j7i4OGRkZGDJkiVSPvoOHTrg1KlTAIBx48ZpfdmKhUeIiKzH4Kyb5tmXMplM5zrJycnIysoCANTU1KBLly5SPvrGxkYolUpcunTJqHz0RERkWQbv6G/evIlRo0ZJL0yNHDkSjY2NGi9MpaSkYObMmZDL5aipqcGsWbMAPM5HDwCenp5wcnLCW2+9pXXGjfrQzblz59C3b19L9hGXL9zSuSxU7mvRY9lDO4jI8egrPGLwhakDBw4gLy8PW7duBQDs2rULJ06cQEZGRot1d+/ejYyMDBQUFEj5bioqKjSSmv3rX/9qkdRMnb5J/6Z6ddIunct2fDrToseyh3YQkeMx64Wp1khqRkRE1tPuk5rpo+sOnHffRGRPmNSMiMjBGTWPPiEhAQkJCRqfqdeMzc/P17rdiBEjcPr0aTOaZzx949/2gOP3RGQtRiU1IyIi+2XRUoL2yJRvAvb+7YGI2hfe0RMRObh2f0dvD0z5BsFxfSJqxsIjREQOzmCgby48kpubi9LSUuzbtw+lpaUa66gXHlm0aBGWL18OABqFR/Ly8vDb3/4WKpXKOj0hIiKtWHiENJj6oJlDRUS2y+AdvbbCIz8vHqKv8IihbYmIyLps4mGsevbKM2fOIDo6uo1bZF1VVVXw9/e36jGio/9q1f0bOl5r9LGtsY+OwVH6aFbhEWOSmjWvExwcjMbGRty7d08qPGJMQjT1wiPWyF5pa9hHx8A+Oob20EeDQzfqSc3q6+uhUCiQmJiosU5iYiJ27NgB4HFa4/j4eKnwiEKhYOERIqI2ZJGkZuqFR3x8fKBQKAA8Ljzy8ssvo3///nBxccHGjRu1Fh4hIiIrEjZmy5Ytbd0Eq2MfHQP76BjaQx8NVpgiIiL7xlw3REQOzqYCvaFUC44gNDQUAwcORFRUlMNMI509ezYCAgIwYMAA6bPbt2/jF7/4BXr37o1f/OIXuHPnThu20Hza+rhq1SoEBQUhKioKUVFR+Oyzz9qwhea5du0a4uLi0L9/f0RGRuKvf308XdaRzqOuPjrSedSprceOmjU2NoqwsDBRXl4ulEqlGDRokCgpKWnrZllcSEiIqKqqautmWFRBQYE4efKkiIyMlD5bunSpSE9PF0IIkZ6eLpYtW9ZWzbMIbX189913xZ///Oc2bJXlVFZWipMnTwohhLh//77o3bu3KCkpcajzqKuPjnQedbGZO3r1VAsdO3aUUi2Q7Xv22Wfh4+Oj8Vl2djZeffVVAMCrr76KTz/9tC2aZjHa+uhIAgMD8cwzzwAAunTpgn79+qGiosKhzqOuPrYHNhPo20u6BJlMhueffx5DhgyR3gZ2RDdv3kRgYCAAoFu3brh582Ybt8g6MjIyMGjQIMyePduuhzXUXb58GcXFxRg+fLjDnkf1PgKOeR7V2Uygby+++uor/Pvf/0Zubi42btyIY8eOtXWTrE4mk0Emk7V1MyzuN7/5DcrLy3Hq1CkEBgZiyZIlbd0ks9XW1mLy5Mn46KOP4OnpqbHMUc7jz/voiOfx52wm0BubLsHeNfcpICAAL730EgoLC9u4RdbRtWtX3LhxAwBw48YNBAQEtHGLLK9r165wdnaGk5MT5s6da/fnsqGhAZMnT8Yrr7yCX/3qVwAc7zzq6qMjnUdtbCbQG5Nqwd49ePAANTU10r+PHDmiMYvDkainxdixYwcmTpzYxi2yvOYACAD//Oc/7fpcCiGQkpKCfv36YfHixdLnjnQedfXRkc6jTm39NFjdoUOHRO/evUVYWJh477332ro5FldeXi4GDRokBg0aJPr37+8wfZw2bZro1q2bcHFxEUFBQWLr1q2iurpaxMfHC7lcLp577jlx69attm6mWbT1ccaMGWLAgAFi4MCB4sUXXxSVlZVt3UyTffnllwKAGDhwoHj66afF008/LQ4dOuRQ51FXHx3pPOrCN2OJiByczQzdEBGRdTDQExE5OAZ6IiIHx0BPROTgGOiJiBwcAz0RkYNjoCcicnAM9EREDu5/AVi4kjpdSo29AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide\n",
            "  return n/db/n.sum(), bin_edges\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f//gHIiIijJbftm0bSktL0bt3b3To0AHjx4836AoJCwtDeno65HI5Fi9ejC+//BIeHh5o06YNPvzwQ0ycOBEdOnTAzp07pdlclcLDw3H79m2pG+jevwHzP5fw8HAolUo8/PDDePnllzFy5MgqZaZNm4auXbtCoVCgd+/eGDRokMHyt99+G/fffz8GDBgAd3d3xMbGory83KzzujZt2rRBUlIS1Go1fHx84OXlhdjYWIMfCjVZtmwZpk+fjvbt2xvMNtJn7LPRV1ZWhujoaMTGxqJv377o0aMHVq1ahZiYGGg0GpO/QxqNBgsXLoRcLoeXlxdyc3Mb9XRymRBMTENkii1btmDjxo344YcfbF0VZGVloVu3btBqtQY/BhyVPX02jRHvCIiIHBwbAiIiB8euISIiB8c7AiIiB8eGgIjIwbEhICJycGwIiIgcHBsCIiIHx4aAiMjBsSEgInJwbAiIiBwcGwIiIgfHhoCIyMGxISAicnBsCIiIHBwbAiIiB8eGgIjIwTXK1EZyuRx+fn62rgYRUaOSlZWF69evV3m9UTYEfn5+OHHihK2rQU3MZqeHjS6bWZ5sxZoQNYzQ0NBqX2fXEBGRg2NDQETk4NgQEBE5uHppCBITExEQEAClUom4uLgqyzUaDSZNmgSlUomwsDBkZWUBqBi4aNmyJYKDgxEcHIy//vWv9VEdIiIyg8WDxTqdDk8//TQOHjwIX19fDBgwACqVCr1795bKbNq0CR06dEBGRgbUajViY2MRHx8PAPD390dKSoql1SAiojqyuCE4fvw4lEolunfvDgCYPHkyEhISDBqChIQELFu2DAAwfvx4PPPMMxBCWPrWRGaraWZQXdfjjCJq7CzuGsrOzkbnzp2lv319fZGdnW20jIuLC9q1a4f8/HwAQGZmJvr164fw8HAcOXLE6Pts2LABoaGhCA0NRV5enqXVJiKi/7HpcwTe3t64ePEiPDw8cPLkSYwZMwapqalo27ZtlbLz5s3DvHnzABifC0tEROaz+I5AoVDg0qVL0t+XL1+GQqEwWqasrAy3bt2Ch4cHXF1d4eHhAQAICQmBv78/zp07Z2mViIjIDBY3BAMGDEB6ejoyMzNRWloKtVoNlUplUEalUmHr1q0AgC+//BLDhw+HTCZDXl4edDodAODChQtIT0+XxhqIiMg6LO4acnFxwYwZMxAQEAAhBIYPH47AwEAsXboUoaGhUKlUiI6ORlBQEJo1awZXV1fs378fAPD9999j/vz5KCgogEwmw6JFi+Du7m7xThFZEweSqbGz+I5Ap9Nh69atOHPmDO7evYurV68iLS0Ny5cvl+4MduzYgaioKGi1WmzcuBHr1q0DAPTq1QteXl4oLCzE6dOnsW3bNukOgYiIrMOm00cTEhIwefJkuLq6olu3blAqlTh+/DgGDx5sabXIAdR1KigRGbK4Iahu+uixY8eMltGfPpqdnY1BgwYZrHvv1FOyL3W9+NbURdKUL+h12Td2J5G1NZow1Bs2bMCGDRsAAGfOnOEUUlvpX7fV1tX0edVxm01VjceKyAKV4X3uZXFDYM70UV9fX4Ppo6asW0n/OQIiIqo/Np0+qlKpoFarodFokJmZifT0dAwcONDSKhERkRlkwoygP4mJiViwYAF0Oh3mzJmDhQsXAgD279+P559/HmVlZWjdujWKi4tRXFyMZcuWYe7cudi3bx+io6NRVFQEFxcXfPzxx4iOjgYAdOvWDdnZ2XBycoJCocDPP/+Mjh071lgPpqokanjFZzKMLmt5n9KKNaH6YixVpckNgU6nQ8+ePQ2ijO7atctgdtBHH32E3377DevXr4darcZXX32F+Ph4nDp1Cp06dYKPjw/++OMPREZGSoPCw4YNw9tvv21Wn39oaChTVRI1sP8Oftzosr4/J1ixJlRfjF07TR4jsGSaaL9+/aQygYGBKC4uhkajgaura133h4jqSU0XfHIMJjcElkwTlcvlUpndu3ejf//+Bo3AzJkz4ezsjHHjxmHJkiWQyWR13iGipqyuv9Lr+2LPu4WmxarTR1NTUxEbG4ukpCTptR07dkChUOD27dsYN24ctm/fjmnTplVZV3/6KMNQE1XFX/ZUVyY3BJZME60sP3bsWGzbtg3+/v4G6wBAmzZtMGXKFBw/frzahoBhqMlR8IJO1mZyQ6A/TVShUECtVmPnzp0GZSqniQ4ePNhgmmhBQQGioqIQFxeHBx54QCpfVlaGgoICyOVyaLVa7N27FxEREfW3d0Rkdew2anxMfo7AxcUFa9euRWRkJHr16oWJEydKUUb37NkDAJg9ezby8/OhVCrx7rvvSons165di4yMDCxfvlxKVJ+bmwuNRoPIyEj06dMHwcHBUCgUmDt3bsPsKRERVcus5wjsBaePUlPWlLuGeEdgWxZPHyWi+tOUL/bU+LAhIGpAvOAb4viBfWJDQER2oS6NJhuP+mFW0LnExEQEBARAqVRKA8H6NBoNJk2aBKVSibCwMIOQp6tXr4ZSqURAQAAOHDhg8jaJ7N1/Bz9u9B9RY2DyHYFOp8PTTz9tEGtIpVIZhJjYtGkTOnTogIyMDKjVasTGxiI+Ph5paWlQq9VITU1FTk4OIiIicO7cOQCodZtE9oAXdftU18+FdxKGrBJryFhKSgC1bpPIGGMXgbp+yXmxJ0dllVhDNaWkrG2blZihjEzGc4Nq46DnSINlKLMWZigjImoYJg8WmxNrCIBJKSnNSVVJREQNwyqxhlQqFaZMmYIXX3wROTk5UkpKIUSt26wOM5QREZnPWIYykxsC/VhDOp0Os2bNkmINhYaGQqVSYfbs2YiJiYFSqYS7uzvUajWAimQ0EydORO/eveHi4oJ169bB2dkZAKrdZm38/PwYYqKB3Zw7strXO3ySVO3rRGT/jI2tMtYQVYsNAVHTY+zaadYDZURE1PRY5cnigwcPIiQkBPfffz9CQkLw73//W1pn2LBhCAgIMAhPTURE1mOVJ4vlcjm++eYb+Pj44I8//kBkZKT0HAFQka6SzwUQEdmGVZ4s7tevn1QmMDAQxcXF0Gg0BgnsqWEY6+tviO3VNH5Q13pwTIKo4ZncNVTdk8X6v+rvLaP/ZLG+3bt3o3///gaNwMyZMxEcHIw33ngDxsauN2zYgNDQUISGhjJ5PRFRPbLqYHFqaipiY2Px8ccfS6/t2LEDv//+O44cOYIjR45g+/bt1a47b948nDhxAidOnICnp6e1qkxE1ORZ5cniyvJjx47Ftm3b4O/vb7AOALRp0wZTpkyRgtEREZF1mNwQ6D9ZXFpaCrVaDZVKZVCm8sliAAZPFhcUFCAqKgpxcXF44IEHpPJlZWXSU25arRZ79+5FUFBQfewXERGZyCpPFq9duxYZGRlYvnw5li9fDgBISkpC69atERkZCa1WC51Oh4iICMydO7dh9rSOrDk4Wtft2cuAan0PTNe2TXvZb6LGzqzoo05OTpDJZJDJZFKIiMoLOwDIZDI4OTlV+f+SJUvg7OyMTZs2wdnZGR9++CE6duwIAFi5ciUWLFgAnU4Hb29vabtERGQdJncNVT5H8O233yItLQ27du1CWlqaQRn95wheeOEFxMbGAoBBhrLExETMnz8fOp3OpG0SEVHDMrkh0H+OoHnz5tJzBPoSEhIwffp0ABXPESQnJ9eYocyUbRIRUcNihjJL1Hcd6ro9ezgWtuCo+01URw2aoSwxMRELFixAVlYW1q1bh9WrVxss1+l0WLt2LZYtWwYPDw907doVAJCbm4stW7ZI0fDc3d2N5itmhjIiooZh8XME+v38Q4cOxZdffom0tDSD5whycnIghJDGDr7//nsoFAp4eXmhRYsWSElJQUpKCkaMGMEMZUREVmZxhjL9fv4xY8Zg27ZtSEhIQLdu3aTnCK5fv478/HxoNBqEhIQgLy8PAwYMQFZWFkpLS5mhjIjIChosQ9n48eNRXl4OAJg9eza2bt2KuLg4BAQESM8R3Lp1C0888YSUoaxjx44oKCiAi4sLnJyc0KtXL8hkMkRHRxvNUKY/RtC6dWsmpqE60W580uiyZnN2WbEmRNZnbGzVrFhDo0aNwrlz53D+/HksXrwYQEUU0so+/xYtWuDZZ59FTEwMjh8/LkUqBYDnnnsO58+fx9mzZ9GqVSsAgLe3N7Kzs1FSUoIffvgBiYmJKCwsrPa9GWuIiKhhWDxYbE4MIl9fX4OxA5lMJkUhDQkJgb+/P86dO2cfM4KI7BDvaKghWBx91JIYRHl5edDpdACACxcuID093eAugoiIGp7FdwSWxCD6/vvvsXTpUjRr1gxOTk5Yv3493N3dLd4pIjLEOwmqSb08RzBq1CiMGjXK4DX9GEQtWrTAF198UWW9cePGYdy4cfVRBSIiqiOrJqYhIiL7Uy93BERkezV1/xDVhA0BkYMz1oBw7MBxsCEgsjP28sueA8yOgw0B0f/wwmc6HqumpV4GixMTExEQEAClUom4uLgqyzUaDSZNmgSlUomwsDCDUKirV6+GUqlEQEAADhw4UB/VISIiM1jcEDRE5jIiIrIei7uG9KOPApCyjOnnFUhISMCyZcsAVGQue+aZZ2rMXDZ48GBLq0Vk1+xlHKAhsNuo8bG4IWiozGVEdWXti2xTvqjXt/o+VmxY6odZDUFlJjKdToc5c+Zg4cKFBss1Gg3Wrl2LM2fO4JdffkF8fDz8/Pxw584djBo1CkIING/eHMXFxdI6K1euxBtvvIGWLVvi0qVLGDJkSLXvbZepKslxrOf5Zpf4uZjF4lSVlWMBBw8ehK+vLwYMGACVSmUQfXTTpk0AgIULF6Jbt26IjY1FfHw8vL29sWjRIqhUKqSkpKB///7w8PCAQqGARqPB559/jtDQUERGRjJVJRGRlZk8WKw/FtC8eXNpLEA/+uhXX32F/Px8qFQqjB8/HsnJyRBCIDo6Gvv37wcAnD59Gi4uLigtLYVKpUJubq6UpSw9PR0DBw5ssJ0lIqKqTL4jMDYWoB99NCsrC88//7wUfbRZs2bIz883iD4KVISudnV1RWBgIDw9PTFs2DA4OTlhwoQJcHKqvm26t2vovvvus2S/iYgcjrFUlTIhhDBlA19++SUSExOxceNGAMD27dtx7NgxrF27VioTFBSExMRE+Pr6AgD8/f1x7NgxyOVyAEBqaipUKhWSkpLg7+8PoKKBUSgUuH37NsaNG4fo6GhMmzatxrqEhoYyVSU1WeLU3+u0nqzfG/VcE2pqjF07Tb4jsCQTWWX5sWPHYtu2bVIjULkOALRp0wZTpkzB8ePHa20IiKgqNiBUVyY3BPpjAQqFAmq1Gjt37jQoU5mJbPDgwQaZyAoKChAVFYW4uDg88MADUvmysjIUFBRALpdDq9Vi7969iIiIqL+9I7IxYxdne7r4NoY6UsMyebBYfyygV69emDhxojQWsGfPHgDA7NmzkZ+fD6VSiXfffVcKN7F27VpkZGRg+fLlCA4ORnBwMHJzc6HRaBAZGYk+ffogODgYCoUCc+fObZg9JSKiapk8RmBPOEZA9qSuXTKNWU13CzUdD95l2JbFYwRERJUcsfFrypiqkojIwfGOgIisht1G9okNAdH/sLuDHBUbAiKyC7xbsB02BERk9/isQ8Mya7C4IVJS1rZNovokTv3d6D8iR2VxGGr9sNH6KSnVarUUhlo/JWVOTg4iIiJw7tw5AKh1m0Tm4kXdcTCsRv0wuSFoiJSUAGrdJjV9vHCTtVlzPKIxjH1YHIbaWBlTU1LWts1KzFBGRNbxbRN9r3rIUGZrzFBGRNQwzApDferUKQQEBECn00GpVCI8PNygjLe3N2bMmIGsrCy4u7vjxo0b8PDwQFFREWJjY/Hmm2+iefPmkMlkmDNnDgDgX//6F44cOYKWLVsiNzcXM2fOrN89JCKiGpncEPTv3x+//fYbDh06hJCQEHh6emL+/PkGZdzd3fH7778jIyMDzz33HK5cuQKZTIZRo0bh6NGjOHHiBL777juMHj0aAwcOhBACxcXFWLNmDR599FEMGDAAU6ZMqbUucrkcfn5+Zu8sEZEjM5ahzOSG4Ndff0WfPn0wd+5c6HQ6PPjgg0hNTcWJEycQGhoKlUqFGzduwMvLC0UEpZUAABmTSURBVEqlEh06dMDdu3chhMD48eNx9uxZ9O7dG87OzmjZsiXKysrg6uqKHj164Nlnn0WzZs0wa9YsBAYG1loXPz8/Rh8loDDe/HXaTqr/ehA1EsbGVs0aLO7Xr1+NqSqvXLlSJVVlfn4+5HI5Fi9ejMWLF+PLL7/E+vXr4erqCqDiLkIIAWdnZ5SXl0MIAZlMVuX99QeL8/LyTK02ERHVwqqDxampqYiNjUVSUpL02o4dOwxyFm/fvr3aVJX6g8WcMeRA6vKrvyHUVI+a7jKMrcc7E7IjJj9ZbE7OYgAW5SwmIiLrYc5iokp1vfuoy3p1vcMgagAmNwT6OYt1Op00sLt06VJpsHj27NmIiYmBUqmEu7s71Go1AMOcxcuXLwcAJCUloXXr1oiMjIRWq4VOp0NERARzFlPDspeuJiI7wpzFZN944TYd7ySoFsxZTPaLF3sim2LOYiIiB8c7AqKmggPQVEe8IyAicnDMUEbkCArjjf8jh8cMZWQdvODYLz797PCYoYyIqscxB4fRaDKUOQRrf/H4K53qytrnjrHzn41VvWg0s4aYqvItW1eAyIbqcv7zO3Mvi1NVmhN0ztfX1yDoXE3r1rbNSkxVSUTUMEyeNaQfdK60tBRqtRoqlcqgTGXQOQAGQedUKhXUajU0Gg0yMzORnp6OgQMHmrRNIiJqWGYFnZsxYwYCAgIghMBDDz1UJehcdHQ0goKC0KxZM7i6umL//v0AgJycHFy/fh1t2rSBk5MTXn/9dTg7OwMA3NzcEBAQAJlMBnd3d3h6etZaF6aqtJ1yUWB0mZOsvRVrQkTmsjhVpU6nw9atW3HmzBlpqmdaWpoUTRSoSDITFRWF9evXQ61WY926dRg6dCjkcjl++eUX+Pj44I8//kBkZCQWLVoEoCJD2U8//WRWnz9TVdrOXe2/jC5r3ewJK9aEiMxl7DprcteQ/vTR5s2bS1M99SUkJGD69OkAKqaPJicnQwiBfv36wcfHBwAQGBiI4uJiaDSauu4LERHVI5Mbguqmj1ZOAa2ujP70UX27d+9G//79pZzFADBz5kwEBwfjjTfegLGo2Bs2bEBoaChCQ0OZs5iIqB4xZzFVq6YuIHthrI7soiIyD3MWExE5OKtMH60pZ3HlCHZlzuKgoKD62C8iIjIRcxY7sMbQ/UNEDY85ix2YvTQEde3Tr0v9OX5Ajow5ixs5e7loW1t973dDHEc2LtTYMUMZEZGD4x2BHeGv/qaHU1ypMWCqSiIbuKv9l9F/RNbGVJUW4JeWAPsZx+BdBtUVU1USNRF1DQjI7iuql1SViYmJWLBgAbKysrBnzx4MHjzYINbQxYsXcerUKWzatAkeHh7o2rUrsrOzkZubi+TkZAQHBwOoiERqrBFghjIiS6yy0jpkzyzOUGaMfpfR6NGjkZCQgJiYGIMLelpaGlq3bo2jR49CrVbjtddew8SJEwFUhJZISUkBAGzfvt1ozmJmKCMiahgWxxrS7zLq3LkzwsPDkZCQYBBr6MqVK+jZsyeAii6jy5cvw8fHB15eXtBqtVW2SURE1mPyHYF+rCGFQgG1Wo2dO3fi9OnTUpeRSqXC7t274ezsbBBrSAiBw4cPQ6PRICcnB0II+Pv7o2PHjigqKkJgYCA8PDyQk5NTJcdBdZihjIjIfBZnKDMWa+j111/HrVu3AACzZ8/G1q1bsX37dhw/flyKNeTq6opRo0ahd+/ecHFxgVwuh7OzMzp37oydO3di6dKluHDhAm7fvm0wDqFPf4ygdevWDDFBRGQmY2OrFsca+vnnn7Fs2TLp2YDVq1cDgJSKEgAiIyOxbNkyDB48GGVlZfDy8kJeXh5kMpnBtoYNG4a333671oFgxhoiIjKfsWunxSEmLAlPnZeXB51OBwC4cOEC0tPTpamkRERkHRbPGnJxccGMGTMQEBAAIQSGDx9eJTx1dHQ0goKC0KxZM7i6umL//v0AgO+//x7z589HQUEBZDIZFi1aBHd3d4t3ioiITGfxHYFOp8PWrVtx5swZ3L17F1evXkVaWhqWL18u3Rns2LEDUVFR0Gq12LhxI9atWwcA6NWrF7y8vFBYWIjTp09j27Zt0h0CERFZh8UNgf700ebNm0tPB+tLSEjA9OnTAVRMH01OTq71iWMiIrIOixuC6p44zs7ONlpG/4ljU9YlIqKG1WjCUOtPH83Ly7NxbYiImg6L7wiMPXFsrIz+E8emrFtp3rx5OHHiBE6cOAFPT09Lq01ERP9j0+mjKpUKarUaGo0GmZmZSE9Px8CBAy2tEhERmcGirqEbN25g0qRJKCkpQVBQEDp27Ig5c+ZUmT7avHlzfPbZZ/j000/h6+uLQ4cOAajIRZCdnY22bdtCJpNh8+bNcHZ2rpcdIyIi01jUEMTFxeHhhx/GwYMHERcXh5s3b2Lx4sUAgOXLlwOoaCxWr16NixcvQiaTISQkBB06dJC2kZiYyJDSREQ2ZFHXkP600OnTp+Prr7+uUubAgQMYMWIE3N3d0aFDB4wYMQKJiYmWvC0REdUjixqCa9euwdvbGwDg5eWFa9euVSlT2xTRmTNnIjg4GG+88QYsDHtERER1UGvXUEREBK5evVrl9ZUrVxr8LZPJqgSRq82OHTugUChw+/ZtjBs3Dtu3b8e0adOqLcsMZURElqlzhrLKgd3qdOrUCVeuXIG3tzeuXLmCjh07VimjUChw+PBh6e/Lly9j2LBh0jKgIkvZlClTcPz4caMNATOUERE1DIu6hvSnhW7duhWPP/54lTKRkZFISkrCzZs3cfPmTSQlJSEyMhJlZWVSggStVou9e/ciKCjIkuoQEVEdWJSPID8/HxMnTsTFixfRtWtXfP7553B3d8eJEyewfv16bNy4EQDw6aefYtWqikTYixcvxsyZM3H37l0MHToUWq0WOp0OERERePfdd02aPsoMZURE5jOWoczixDS2wMQ01BDWp+0wuuyvvadasSZEDaPBEtMQEVHj1miCzhHZEu8WqCkzqyFITEzEggULoNPpMGfOHCxcuNBguUajwbRp03Dy5El4eHggPj4efn5+OHjwIBYuXIjS0lI0b94cb731FoYPHw6gIk/xlStX0LJlSwBAUlJStbOPiOpDTRd0IkdlckOg0+nw9NNP4+DBg/D19cWAAQOgUqnQu3dvqcymTZvQoUMHZGRkQK1WIzY2FvHx8ZDL5fjmm2/g4+ODP/74A5GRkQYPle3YsYPPBRAR2YjJYwSWZCLr168ffHx8AACBgYEoLi6GRqOpx90gIqK6MvmOoLpQEceOHTNaRj8TmVwul8rs3r0b/fv3h6urq/TazJkz4ezsjHHjxmHJkiXVPqHMxDRkKmt3/3D8gBo7q84aSk1NRWxsLD7++GPptR07duD333/HkSNHcOTIEWzfvr3adZmYhoioYZjcEFiSiayy/NixY7Ft2zb4+/sbrAMYhpkgIiLrMblrSD8TmUKhgFqtxs6dOw3KVIacGDx4sEEmsoKCAkRFRSEuLg4PPPCAVL6srAwFBQWQy+VSmImIiIj62ztq0hrDDCBjdWSXEdkTk+8IXFxcsHbtWkRGRqJXr16YOHGilIlsz549AIDZs2cjPz8fSqUS7777LuLi4gAAa9euRUZGBpYvX47g4GAEBwcjNzcXGo0GkZGR6NOnD4KDg6FQKDB37tyG2VMiIqqWWc8RODk5SeGmK2MCVWYiAypCUTs5OVX5/5IlS+Ds7IxNmzbB2dkZH374ofSswMqVK6VnE7y9vZmqkgw0hl/9dVHXAWYOTFNDsMpzBGlpaVCr1UhNTUVOTg4iIiJw7tw5AKh1m9T0NdWLfV3xeJC1mdwQ6D9HAEB6jkD/op2QkIBly5YBqHiO4JlnnoEQAgkJCZg8eTJcXV3RrVs3KJVKaVC4tm1S08CLW8Or6zHmnQRZ5TmC7OxsDBo0yGDdyieLa9sm2QYv3I6DA9rUaILOMVUlkXVtxHu2rgLVszqnqqxkznMEvr6+Bs8R1LRubdusxFSVREQNw+Tpo/rPEZSWlkKtVkOlUhmU0U9dqf8cgUqlglqthkajQWZmJtLT0zFw4ECTtklERA3L5DsCFxcXzJgxAwEBARBC4KGHHpKeIwgNDYVKpUJ0dDSCgoLQrFkzuLq6Yv/+/QCAnJwcXL9+HW3atIGTkxNef/11aZqom5sbAgICIJPJ4O7ublL4CKaqJKo/mYU3q329W9sOVq4JNTRjqSrNmj66detWnDlzRprqmZaWZvAcwY4dOxAVFYX169dDrVZj3bp1GDp0KORyOX755ReDMNSLFi0CALi7u+Onn34yq8/fz8+PqSqJ6klM4hfVvr79kQlWrgk1NGPXWatMH+3Xr59URj8MtX4EUiJqOMYu9nVdh41E08Iw1ERNRF0u9kQAw1ATETk8q0wfrSxvahjqadOmWbRTRE2VvfzqZ7dR08Iw1ERUr9hIND5mTR+tDEOt0+kwa9asKtNHZ8+ejZiYGCiVSri7u0OtVgMwDENdOcsoKSkJrVu3RmRkJLRaLXQ6HSIiIhiGmhyevfzqbwhsJOyTTAghbF0Jc4WGhnL6KDVqTfliX1dsCBqesWunVQeLiYjI/jSaoHNE1LSx28h22BAQWYjdPA2PTz83LLMagsTERCmt5Jw5c7Bw4UKD5RqNBtOmTcPJkyfh4eGB+Ph4KSbQ6tWrDVJVRkZGmrRNInvAi7194l1E/WCqSiJqkuraeDtiA8JUleRQ+MueauOIdxn1EmuosnsnKysLe/bsweDBgw1iDV28eBGnTp3Cpk2b4OHhga5duyI7Oxu5ublITk5GcHAwgIpIpMYaAWYoIyJbC13ypq2rYBGLM5QZo99lNHr0aCQkJCAmJsbggp6WlobWrVvj6NGjUKvVeO211zBx4kQAFaElUlJSAADbt283mrOYGcqIiBqGyc8RGIs1pN9l1LlzZ4SHhyMhIcEg1tCVK1fQs2dPABVdRpcvX4aPjw+8vLyg1WqrbJOIiKzH4lhDp0+flrqMVCoVdu/eDWdnZ4NYQ0IIHD58GBqNBjk5ORBCwN/fHx07dkRRURECAwPh4eGBnJwcJCQkVPv+93YN3XffffWw+0REjsNYhjKzQkzs378fzz//vBRraPHixRg/fjxu3bqFgwcPoqSkBEOHDsXZs2cREBAAtVqN7t27IygoCKNGjcLu3bvh4uKCwsJC/P7772jTpg2++uorLF26FEVFRbh9+zYuXbqEtm3b1lgPhpighhD2xkGjy479fYQVa0LUMIxdO80aIxg1ahRGjRpl8NpLL70kzRRq0aIFxo4dCwBSKkqgoltp7NixWLNmDcrKyuDl5QUPDw/IZDJMnjwZkydPBgAMGzYM586d40Aw2Z2aGomasAGhxsDiWEP6XUalpaVQq9VQqVQGZSrDUwMw6DLKy8uDTqcDAFy4cAHp6enSVFIiIrIOi2cNWRKe+vvvv8fSpUvRrFkzODk5Yf369XB3d7d4p4iIyHQMQ030P3Xt/qkJu4bInjAMNRERVYvRR8mhNMSv/rq8H+8UyJ6wIaAmx9oXe6LGjl1DREQOjncE1Gjxlz9R/eAdARGRg6uXO4KGyFxG1JQxnAXZk3oNQ11fmcucnZ0trRY1IuziMcRGgqzN4oagITKXDR482NJqETkcxkOiurK4Iagpc1l1ZfQzl2VnZ2PQoEEG62ZnZ1f7PsxQRgSEJiyqvZAdbJPsU4NlKLMWZigjImoYFs8aMpa5zFgZ/cxlpqxLREQNy+I7ggEDBuC///0vunfvDplMhqKiIhw6dMigzKOPPoro6GjIZDIAQFhYGGQyGUJCQvDII49ArVZDq9Xi8uXLGDhwYK3vKZfLpVlHRERkGmMZyixuCGQymZSOUv81/TDUlQ0AAINyPXv2hKenJ+7cuQMXFxfEx8ebNGPIz8+P0UebkOljttdpva1fx9RzTYiaNmNjq/Uya6hPnz44cOAAgIrnAhISErB8+XKpzP79+/HZZ59h8ODBUoayygbB09MTf/zxh6XVICKiOrJ4jKC6WUP3zvwxNmsIADIzM9GvXz+Eh4fjyJEjRt9nw4YNCA0NRWhoKPLy8iytNhER/Y9NZw15e3vj4sWL8PDwwMmTJzFmzBikpqZWm7xef9YQp44SEdUfixsCc2YN+fr6GswakslkcHV1BQCEhITA39+fyevJZMbGFjh2QGQeJq8nInJwTF5PTU5Ns5B4t0BUFZPXk1XUdYpofWNDQI6MyeuJiKhabAiIiBxcowk6R1QfOH5AVBUzlFG9sZdxACIyj8VdQ5UZyr799lukpaVh165dSEtLMyijn6HshRdeQGxsLAAYZChLTEzE/PnzpemkRERkHcxQRmZpyr/62W1EjqrRZCijumnKF24iqh+NZrDY3lJV5uXlwdPT06Z1MIZ1M19t9QoN/cCKtTFkr8cMYN3qwpb1arBUlZbEGjInQ5m9paq054faWDfz2Wu9ANatruy1bvZYL5vGGlKpVFCr1dBoNMjMzER6erpJGcqIiKj+2DTWUGBgICZOnIjevXvDxcUF69atMylDGRER1SNBdfLxxx/bugpGsW7ms9d6CcG61ZW91s0e69Uog84REVH9YawhIiIHx4agjt555x3IZDJcv34dACCEwHPPPQelUok+ffrg119/tWp9XnnlFdx3333o06cPxo4di4KCAmnZ6tWroVQqERAQgAMHDli1XpUSExMREBAApVKJuLg4m9Sh0qVLl/DQQw+hd+/eCAwMxAcfVEwNvXHjBkaMGIEePXpgxIgRuHnzpk3qp9Pp0K9fP4wePRpARV7vsLAwKJVKTJo0CaWlpTapV0FBAcaPH4/77rsPvXr1ws8//2w3x+y9995DYGAggoKC8OSTT6KkpMRmx23WrFno2LEjgoKCpNeMHSdbXzcktu2ZapwuXrwoRo4cKbp06SLy8vKEEELs27dPPPLII6K8vFz8/PPPYuDAgVat04EDB4RWqxVCCPHqq6+KV199VQghRGpqqujTp48oKSkRFy5cEN27dxdlZWVWrVtZWZno3r27OH/+vNBoNKJPnz4iNTXVqnXQl5OTI06ePCmEEKKwsFD06NFDpKamildeeUWsXr1aCCHE6tWrpWNobe+884548sknRVRUlBBCiAkTJohdu3YJIYR46qmnxEcffWSTek2bNk188sknQgghNBqNuHnzpl0cs8uXLws/Pz9RVFQkhKg4Xps3b7bZcfvPf/4jTp48KQIDA6XXjB0nW183KrEhqINx48aJlJQU0bVrV6khmDdvnti5c6dUpmfPniInJ8cm9fvXv/4lpkyZIoQQYtWqVWLVqlXSspEjR4qffvrJqvX56aefxMiRI6W/762TralUKpGUlGTwmeXk5IiePXtavS6XLl0Sw4cPF8nJySIqKkqUl5cLDw8PqZG/91haS0FBgfDz8xPl5eUGr9vDMbt8+bLw9fUV+fn5QqvViqioKJGYmGjT45aZmWnQEBg7TvZy3WDXkJkSEhKgUCjQt29fg9erC7Vhq3AZn376KR599FG7qZc91MGYrKwsnDp1CmFhYbh27Rq8vb0BAF5eXrh27ZrV6/P8889jzZo1cHKq+Grm5+ejffv2cHGpmOltq2OXmZkJT09PzJw5E/369cOcOXNw9+5duzhmCoUCL7/8Mrp06QJvb2+0a9cOISEhdnHcKhk7Tvby3Wg0ISasKSIiAlevXq3y+sqVK7Fq1SokJSXZoFY11+vxxx+X/u/i4oKpU6dau3qNzp07dzBu3Di8//77aNu2rcEymUwGmUxm1frs3bsXHTt2REhICA4fPmzV965NWVkZfv31V/zzn/9EWFgYFixYUGWsxxbHDABu3ryJhIQEZGZmon379pgwYQISExOtXg9T2eo41YQNQTUOHTpU7eu///47MjMzpbuBy5cvo3///jh+/LhZ4TLqu16VtmzZgr179yI5OVk60axRr9rYQx3updVqMW7cOEydOhVPPPEEAKBTp064cuUKvL29ceXKFXTs2NGqdfrxxx+xZ88e7N+/HyUlJSgsLMSCBQtQUFCAsrIyuLi42OzY+fr6wtfXF2FhYQAqogjHxcXZ/JgBFd+Lbt26SfF7nnjiCfz44492cdwqGTtO9vLdYNeQGe6//37k5uYiKysLWVlZ8PX1xa+//govLy+oVCps27YNQggcPXoU7dq1k24FrSExMRFr1qzBnj170KpVK+l1ewjjYUoYEmsSQmD27Nno1asXXnzxRel1/VAoW7dule6yrGX16tW4fPkysrKyoFarMXz4cOzYsQMPPfQQvvzyS5vVC6jozujcuTPOnj0LAEhOTkbv3r1tfswAoEuXLjh69CiKiooghJDqZg/HrZKx42Tr64bE6qMSTYj+YHF5ebmYP3++6N69uwgKChK//PKLVevi7+8vfH19Rd++fUXfvn3FU089JS1bsWKF6N69u+jZs6fYv3+/VetVad++faJHjx6ie/fuYsWKFTapQ6UjR44IAOL++++Xjte+ffvE9evXxfDhw4VSqRQPP/ywyM/Pt1kdv/vuO2nW0Pnz58WAAQOEv7+/GD9+vCgpKbFJnU6dOiVCQkLE/fffLx5//HFx48YNuzlmS5cuFQEBASIwMFBER0eLkpISmx23yZMnCy8vL+Hi4iIUCoXYuHGj0eNk6+tGJT5ZTETk4Ng1RETk4NgQEBE5ODYEREQOjg0BEZGDY0NAROTg2BAQETk4NgRERA6ODQERkYP7fwRBdToOfaAPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xDVFbBAQEYN26dYiLi7N2VTBjxgz4+/tj2bJl1q6KTbClY2MPeEdAROTgGAiIiBwcHw0RETk43hEQETk4BgIiIgfHQEBE5OAYCIiIHBwDARGRg2MgICJycAwEREQOjoGAiMjBMRAQETk4BgIiIgfHQEBE5OAYCIiIHBwDARGRg2MgICJycM4tF7E9Xl5eCAgIsHY1iIjsyrlz53D16tUmr9tlIAgICEB+fr61q0Gt8Fmnxwwum3lnbzvWhMhxxcTE6H2dj4aIiBwcAwERkYMzSyDIyspCcHAwlEol0tLSmizXaDSYNGkSlEolhgwZgnPnzgG4+7zKzc0NkZGRiIyMxPz5881RHSIiagWT2wi0Wi1eeOEF5OTkwN/fH4MGDYJKpUJoaKhUZv369ejVqxeKioqgVquRnJyMjIwMAEBQUBCOHj1qajWIiKiNTL4jyMvLg1KpRGBgIFxcXDB58mRkZmbqlMnMzMT06dMBAM8++yz27t0LIYSpb01ERGZgciAoLS1F3759pb/9/f1RWlpqsIyzszN69OiBiooKAMDZs2cxcOBAxMbGIjc31+D7rF27FjExMYiJiUF5ebmp1SYiov+wavfRPn364MKFC/D09MShQ4cwbtw4FBQUoHv37k3Kzps3D/PmzQNguAsUERG1nsmBQKFQoKSkRPr74sWLUCgUesv4+/ujvr4ev/32Gzw9PSGTyeDq6goAiI6ORlBQEE6dOsULvYPhGAMi6zL50dCgQYNw+vRpnD17FrW1tVCr1VCpVDplVCoVNmzYAAD46quvMGrUKMhkMpSXl0Or1QIAzpw5g9OnTyMwMNDUKhERUSuYfEfg7OyMlStXIj4+HlqtFrNmzUJYWBgWL16MmJgYqFQqzJ49G9OmTYNSqYRcLodarQYAHDhwAIsXL0bnzp3RqVMnrF69GnK53OSdIiIi45mljaBTp06QyWSQyWRwcnICAKSkpEjLZTIZOnXq1OT/EyZMwKlTp7B+/Xo4OTnBxcXFHNUhIqJWsOo4gsLCQqjVahQUFKCsrAxxcXE4deqUFEzI9jT3PJ+I7JPJgaDxOAIA0jiCxoEgMzMTS5YsAXB3HMGLL74IIQQyMzMxefJkuLq64v7774dSqUReXh6GDRtmarWog2BDMpHlmRwI9I0jOHjwoMEyjccRlJaWYujQoTrr3jsGgcgQQ0GCAYKodexmGuq1a9di7dq1AIATJ06wi6m1RFm7Ai1bxXODSK+Ged7uZdVxBMas26DxgDIiIjIfq44jUKlUUKvV0Gg0OHv2LE6fPo3BgwebWiUiImoFq44jCAsLQ0JCAkJDQ+Hs7IxVq1YZ1WOIqSqJmrp9osjgMrffKduxJmSrzJKqMisrCwsWLIBWq8WcOXOwcOFCAMATTzyBJ554AhqNBklJSVAqlfD09MSsWbMAALm5uThz5gzc3Nyg1Wpx7tw5qZdRTk4OnJ2d4ebmhjfeeAPR0dHw8fFpth5MVUnU1C/Dnjbr9gb8lNlyIbIrhtpWjQ4EpowX8PLywvbt2+Hn54djx44hPj5ep3dQeno6G3+JiKzE6DYCU/IODBw4EH5+fgDuPg66ffs2NBqNGXeDiIjayuhAYGregQZbt25FVFSUNOsoAMycORORkZFYunSpwYQ1zEdARGQZ7TqOoKCgAMnJycjOzpZeS09Ph0KhwI0bNzBhwgRs2rQJSUlJTdZlPgKi9tVcmwPbDzoWowOBKeMFGsqPHz8eGzduRFBQkM46ANCtWzckJiYiLy9PbyAgIvM3CBMBrXg0ZMp4gcrKSowdOxZpaWkYPny4VL6+vl7qylRXV4dvv/0W4eHh5tgvIiIyktGBoPF4gZCQECQkJEjjBb755hsAwOzZs1FRUQGlUokPP/wQaWlpAICVK1eiqKgIKSkpiIyMRGRkJK5cuQKNRoP4+HhEREQgMjISCoUCc+fOtcyeEhGRXjJhqHXWhsXExHAcATkkW3k0xDYC+2To2mk3k84Rke1gQ3LHwkBAZGNs5Vc/OY5WTTqXlZWF4OBgKJVK6fl/YxqNBpMmTYJSqcSQIUN0pjxNTU2FUqlEcHAwdu/ebfQ2iYjIstpliglDKSkBtLhNIrIvfGxkf4wOBJZISQmgxW0S2To+yiF7Z3QgsFRKypa22YAZyog6AH5vrcpiGcraCzOUERFZRqummDhy5AiCg4Oh1WqhVCoRGxurU6ZPnz6YMWMGzp07B7lcjmvXrsHT0xO3bt1CcnIyVqxYARcXF8hkMsyZMwcA8I9//AO5ublwc3PDlStXMHPmTPPuIRERNcvoQBAVFYV//etf2LNnD6Kjo+Ht7Y3nn39ep4xcLsevv/6KoqIivPzyy7h06RJkMhmeeOIJ/Pzzz8jPz8e+ffvw5JNPYvDgwRBC4Pbt23j33XfxX//1Xxg0aBASExNbrAszlBERtZ7JGcoOHz6MiIgIzJ07F1qtFiNGjEBBQQHy8/OllJTXrl2Dr68vlEolevXqherqaggh8Oyzz+LkyZMIDQ2Fk5MT3NzcUF9fD1dXVzzwwAN46aWX0LlzZynNZUuYocx41+eOMev2ev09u+VCRGSTTM5QVlpaioEDB2LdunUAgE2bNuHgwYNYuXKlVObSpUvIysqCv78/ACAoKAgVFRXw8vLCokWLsGjRInz11VdYvXq1lI9ALpdDCAEnJyfcuXMHQgjIZLIm79+4sZj5CIiIzKdVA8pM1ZCPYM2aNdJr6enp+PXXX5Gbm4vc3Fxs2rRJ77rz5s1Dfn4+8vPz4e3t3V5VJiLq8IwOBK3JRwDApHwERETUfpiPgIjIwRndRtA4H4FWq5UadhcvXiw1Fs+ePRvTpk2DUqmEXC6HWq0GoJuPICUlBQCQnZ0Nd3d3xMfHo66uDlqtFnFxcQ6Rj8BQA25zDbHNNfqyAZeITMF8BFbQnoGAvYaIqIGha2e7NhYTEZHtYSAgInJwDARERA7ObiadsyRzP0fvyNr6WbFtgch2MUMZEZGDMzoQNGQo27VrFwoLC7FlyxYUFhbqlGmcoezVV19FcnIyAOhkKMvKysLzzz8PrVZr1DaJiMiyjA4EjTOUubi4SNnEGsvMzMT06dMB3M1Qtnfv3mYzlBmzTSIisixmKLMlbd0ne/gs7KGORB0cM5QREZFe7TLpnKF1jdkmERFZltF3BI0nnVMoFFCr1di8ebNOmYZJ54YNG6Yz6ZxKpUJiYiJee+01lJWV4fTp01KGspa2qQ8zlBERtZ7JGcpMmXQuLCwMCQkJCA0NhbOzM1atWgUnJycA0LvNljBDGZH51K17Tu/rnedsaeeakKUZalvlpHNEDo6BwHFw0jkiItKLgYCIyMG1yxQTOTk5iI6ORv/+/REdHY3vvvtOWmfkyJEIDg5GZGQkIiMjceXKFdP2iIiIWsXoxuKG6SBycnLg7++PQYMGQaVSITQ0VCrTeIoJtVqN5ORkZGRkwMvLC9u3b4efnx+OHTuG+Ph4aUAZcDeBfYccIEZEZAfaZYqJgQMHws/PD8DdHkS3b9+GRqMx424QEVFbGR0I9E0x0fhX/b1lGk8x0djWrVsRFRUFV1dX6bWZM2ciMjISS5cuhaFOTGvXrkVMTAxiYmJQXl5ubLWJiKgF7dpYXFBQgOTkZKxZs0Z6LT09Hb/++ityc3ORm5uLTZs26V133rx5yM/PR35+Pry9vdurykREHZ7RbQStmWLC399fZ4qJhvLjx4/Hxo0bERQUpLMOAHTr1g2JiYnIy8tDUlKSSTtFZCtspY++oXoQAe00xURlZSXGjh2LtLQ0DB8+XCpfX1+PyspKeHl5oa6uDt9++y3i4uLMt3dE1GbNBQ8ONutYWjXFxIwZMxAcHAwhBEaNGtVkiompU6ciPDwcnTt3hqurK3bu3Ang7jQSx48fx3PPPQeZTAaFQoGffvoJ7u7uGDZsGC5cuAAhBAYNGoS5c+dabGeJbEVbf6E3dwHmr35qq1Z1H92wYQNOnDghdR8tLCxESkqKVCY9PR1jx47F6tWroVarsWrVKjzyyCN45pln8OWXXyIvLw9lZWWIi4uTHhnduXMHx48fl7Z58uRJnS6pRPT/2crFnncLHQszlBEROTi7yVBGZCxL/GrmIxnj8W7B/thNhjKHSFVJtms1zzez4OdoVSanqjSl+2hz6xqboYypKomILMPoNoLG3Udra2uhVquhUql0yjR0HwXQJEOZWq2GRqPB2bNnpQxlxmyTiIgsyy4zlDFVJRFR6xlKVckMZURkVuLInw0ukw1c2o41oXsxQxkREelllkDQ1oQ1FRUVePTRR+Hh4YEXX3zRHFUhonYgjvzZ4D+yPyYHgoaENbt27UJhYSG2bNmCwsJCnTKNE9a8+uqrSE5OBgB06dIFS5cuxfvvv29qNYiIqI1MHkfQeHQwAGl0cONpIjIzM7FkyRIAd0ccv/jiixBCwN3dHQ8//DCKiopMrQYRmZklft2z/cA2mRwITBlx7OXlZerbEzkUS1yceQEmuxxZzAxlRETmY3IgMDVhjbEajyzm9BJk72ypUdVW6sLHRtZjciAwJWENUUdnKxdZe2foc2SAMA+TA4EpI44BICAgAFVVVaitrcW2bduQnZ3NfARkV3ixJ3vHkcVERuDF3jbxjqB1DF07zdJYnJWVhQULFkCr1WLOnDlYuHChznKNRoOkpCQcOnQInp6eyMjIkOYKSk1Nxfr16+Hk5ISPP/4Y8fHx5qgSkV68oHcsbT2eDCC6TA4EDQPKcnJypHSTKpVK5/FO4wFlarUaycnJyMjIQGFhIdRqNQoKCqQUlqdOnZImpCNqC17sqSVsmNZl1QFlhlJYDhs2zNRqkQPgBZ8swdxBwh6CjlUHlDWXwvJezFBGRNa3y8a31zyTM5RZGzOUERFZhsmTzrVmQBkAo1NYEhFR+7DqgDKVSoXExES89tprKCsrk1JYtoQZyoiIWs9QhjKrDihrLoVlcwICAjiOgGxHVYbhZd0ntX6d5hjaHpERDLWtckAZ2S9DF9P2vli29aLeFgwEZAKLpqpkhjKyKVUZhv9ZYj0iO2fVAWUNGcqOHTuGY8eOmVoVIsthMKAOzOQ7gsYDylxcXKQBZY1lZmZi+vTpAO4OKNu7d69OhrIuXbqYWg0iImojZigjsidtaZgmaoHdDChjhjIyCz7iIWqCGcrItvHCbTzeLVAbmdxG0HhAWW1tLdRqNVQqlU6ZhgFlAJihjIjIxjBDGRGRg+OAMrJtfDRkHnw0RLDRAWXA3QxlSqUSwcHB2L17tzmqQ0T34mA5agYzlJH18WJkm9j47DCYoYzaBy/2tovHxuHZTYYyamdtmdCNFxTHYYljzbsMq7HLAWVMVWlN71m7AtRh8dyyNIulqjRlQFlrMpQxVSURkWVYdUCZSqWCWq2GRqPB2bNnjc5QRkRE5mOXGco6aqrKO6LS4LJOsp7tWBMi6ogMparkgDIrqK77h1m35975GbNuj4g6JosOKCMiIvvFVJVERA7O5EDQMLJ4165dKCwsxJYtW1BYWKhTpvHI4ldffRXJyckAIKWqfP/9902tBhERtRFTVRIROTi7SVXJDGVkaW1txGdjPdk7uxlZbG8ZyszdM4iaastnzIs2UVN2k6qSDGvugsgLHxG1xORA0HhksUKhgFqtxubNm3XKNIwsHjZsGFNVdmCWCEjmvrPinRpRU0xVSUTk4DiyuAX2/guyPR8N2cMdQXvjozmyJYaunWZpLM7KysKCBQug1WoxZ84cLFy4UGe5RqNBUlISDh06BE9PT2RkZEhzBaWmpmL9+vVwcnLCxx9/jPj4eHNUif7DVqazsPcLurmxXYdsCVNVUqvwgt467dnGwQBCbeUwqSp5ASN70dZz1RGDhCPusyWYZUCZTCZDcHAwtFotoqKi4OPjo1OmpKQEy5cvx/Hjx+Hp6YmuXbuioqICx48fh1qtRkbG3bR3NTU1BlNVMkMZkSmWW7sCVuCI+9w8i2Uo02q1+PHHH3H06FH4+/tDqVTi4Ycf1ilTWVmJnj17So+G5syZIy3z8fHB0aNHAQCzZ882+D7MUEZEZBkmzzX022+/wcXFRZprKCQkBBUVFTplamtrMWLECADAuHHjcOvWLcjlcvj6+qKurk4q1/hLl0sAABIISURBVFyqSiIisgyT7wh69OghpZpUKBQ4fvx4kzsCFxcXHDhwAPPmzcO2bdvQtWtXXLt2DXFxcXj//fcRGRkJV1dXlJSUGJWqsqNmKCMisiRDGcpMDgROTk546KGHpAFlgwcPhlwu1xlQ1qtXL1y/fl0aUNYwvURsbCzeeOMNqNVqXL9+HXV1daiurkb37t2bvE/jNgJ3d3e7zlBGRGQNhtpWTX401PAo59SpUyguLkZ0dDQUCgVSUlKkJPb+/v548803UVRUhB9//BHV1dXw9PSEq6sr3nnnHRQXF+P8+fMICwvDqVOn9L7PvHnzkJ+fj/z8fHh7e5tabSIi+g+TA0HjuYZqa2uhVqulANCgYa4hADpzDZWXl0Or1QIAzpw5g9OnT0vdUImIqH2YZa6hGTNmIDg4GEIIjBo1qslcQ1OnTkV4eDg6d+4MV1dX7Ny5EwBw4MABPP/886isrIRMJsMbb7wBuVxu8k4REZHxzJKqcsOGDThx4gSqq6vx73//G4WFhTqPhtLT0zF27FjU1dVh3bp1WLVqFQAgJCQEvr6+qKqqwvHjx7Fx40bpDoGIiNqHVVNVGhpZTERE7cfkQKAvVeW9o4MNpao0Zl0iIrIsu0lVyZzFRESWYZbuo8amqgSgk6rSmHUbsPsoEZFlWLX7qEqlglqtlkYmnz592qiRxUREZD4mPRq6du0aJk2ahJqaGoSHh8PHxwdz5sxp0n3UxcUFX3zxBT799FP4+/tjz549AIAXXngBpaWl6N69O2QyGT777DPmIiAiamcmBYK0tDQ89thjyMnJQVpaGq5fv45FixYBAFJSUgDcDRapqam4cOECZDIZoqOj0atXL2kbWVlZnFKaiMiKTHo01Lhb6PTp07Ft27YmZXbv3o3Ro0dDLpejV69eGD16NLKyskx5WyIiMiOTAsHly5fRp08fAICvry8uX77cpExLXURnzpyJyMhILF26FEIIU6pDRERt0OKjobi4OPz73/9u8vo777yj87dMJoNMJmvVm6enp0OhUODGjRuYMGECNm3ahKSkJL1lmaGMiMg0bc5Q1tCwq0/v3r1x6dIl9OnTB5cuXWqSohK423V0//790t8XL17EyJEjpWUA0K1bNyQmJiIvL89gIGCGMiIiyzDp0VBDt9CsrCxERETgypUrSEtL0ykTHx+P3bt3Y/z48QgMDMTnn3+OkJAQ1NfX46uvvkJ0dDTCw8Px6quvwtnZbsa3ERF1GDJhwoP5iooKTJw4Ebm5uRg8eDC2bt2K+Ph4LFq0CNnZ2Vi3bh0AYOrUqdi+fTu8vb0RFxeH69ev49NPP5Ue73Tq1AkDBw7Evn37jJpighnKiIhazyIZyjw9PfHOO+9gyZIl2L17NwBg8uTJKC4uloIAcHdKiKysLAwbNgz19fXw9fVF165dcfz4camMEAKenp7QaDRwdXVt9n0DAgKYoYza1erCdIPL5odOaceaELWdobZVk5/F6OsVdPDgQYNlGk865+XlJZXZunUroqKiDAYBzjVEtopBguydTTyULygoQHJyMrKzsw2WadxYzB5DZC8MBQkGCLIlVp10rqH8+PHjsXHjRgQFBZlaHSIiaiWT7wgaTzqnUCigVquxefNmnTINvYuGDRumM+lcZWUlxo4di7S0NAwfPtzUqhC1qLnHOESOyuQ7AmdnZ6xcuRLx8fEICQlBQkKCNOncN998AwCYPXs2KioqoFQq8eGHH0pdTFeuXImioiKkpKQgMjISkZGRuHLliqlVIiKiVjCp+6i1xMTEsNcQtYk93BGw/YAsxdC10+Q7AiIism8MBEREDs4muo8SmZM9PP5pTlvrz0dK1FYMBGS37P2Cb24c2EZtZZZAkJWVhQULFkCr1WLOnDlYuHChznKNRoOkpCQcOnQInp6eyMjIkOYKSk1Nxfr16+Hk5ISPP/4Y8fHx5qgSdRC82BNZnsmBQKvV4oUXXkBOTg78/f0xaNAgqFQqhIaGSmXWr1+PXr16oaioCGq1GsnJycjIyEBhYSHUajUKCgpQVlaGuLg4nDp1inmLicysLXcLvMNwHCYHgry8PCiVSgQGBgK4O+lcZmamTiDIzMzEkiVLAADPPvssXnzxRQghkJmZicmTJ8PV1RX3338/lEol8vLyMGzYMFOrRXaEv/qtqy2fP4NEx2LVSedKS0sxdOhQnXWNmYaabBMv6ET2ySxtBBcvXkRwcDC0Wi2ioqKaZCq7c+cOfv/73+P48ePw9PREXV0dAKCkpAQ7duzAihUr4OLiojfDWQOmqiSyD+vwF2tXgQxoc6rKlvj6+uLAgQM4duwY/P390a9fP0yaNEmnjBACzs7OKCoqQnp6OmbNmgVPT0/cd999CA0NRWpqKo4dO4aYmBgsXrxY7/swVSURkWWYPKCsYYaKlhLX65vJYv78+di5cyc0Gg26du2Kuro6DBgwwNQqERFRK5h8R3D58mWMGDEC8fHx0Gq1GD58OOrr67F48WLExMRApVKhU6dOqKurg1KphFwuh6+vLyoqKhAWFoaEhASEhobi9u3biIiIQNeuXVt8T6aqJLJPZ6uuG1x2f/de7VgTx2SRVJUN+vbtK6Wq3LRpEw4ePIiUlBRpuUwmw5o1a+Dv7w8AOnkHFi1ahHHjxkGlUuGrr74y+B6N2wjc3d056RyRjZqW9aXBZSHNrLfp8YnmrwzpsFiqytYkpvH3929zYhpmKCOyHc1d7Mn+MDENEenV3hf75t6PdwuWZXIgaJyYRqvVYtasWVJimoY2gtmzZ2PatGlSG4FarQagm5im4VFSdnZ2s91IicjxGAoSDBDmwcQ0RKSXPTz+YSBoHSamISIivTgNNZGDs4df/oawXcE8eEdAROTgeEdA5ADs+Vc/WR4DAVEHwYs9tRUDARF1SGw/MB5TVRLZEf7qJ0tgqkoiI5j71yUv6NbFuwVdTFVJ9B9tvTjzot6xOGKQsJtUlcxQRkTWFvPmCmtXwSQWy1DWXpihjIjIMkweUNaaaagB6ExDbcy6RERkWVadhlqlUiExMRGvvfYaysrKcPr0aQwePLjF92SGMiKi1jNLhjJ93UQbT0NdX18Pd3d3PP3007h9+zaWLFmCuXPnIiAgAG+++SY+++wzODs7Y82aNQCAsLAwVFVVoVu3bujUqRMUCgUqKipanIY6ICCAs49SuxqyNMfgsoN/Ht2ONSFqO0Ntq0Y/GmroJrpr1y4UFhZiy5YtKCwsBAA88cQTOHXqFF5//XUMHz4cRUVF+OCDD7Bnzx4AgJ+fHwoKCqDRaHDw4EEkJydL2+3Xrx9+/PFH1NTUoLi4mLkIiIjamdGBoHE3URcXF6mbaGOZmZmYPn06gLvdRPfu3QshBAYOHAg/Pz8Ad+8Cbt++DY1GY8bdICKitjI6EOjrJnpvV09D3UQb27p1K6KiouDq6iq9NnPmTERGRmLp0qUwlCdn7dq1iImJQUxMDMrLy42tNhERtaBdu48WFBQgOTkZ2dnZ0mvp6elQKBS4ceMGJkyYgE2bNiEpKanJukxeT8Zq7nl+c/isnxyV0XcEpnQTbSg/fvx4bNy4EUFBQTrrAEC3bt2QmJiIvLy8tu8NERG1mtGBoHE30draWqjVaqhUKp0yDd1EAeh0E62srMTYsWORlpaG4cOHS+Xr6+ulrkx1dXX49ttvER4ebo79IiIiIxn9aKhxN1GtVotZs2YhLCwMixcvRkxMDFQqFWbPno1p06ZBqVRCLpdDrVYDAFauXImioiKkpKQgJSUFAJCdnQ13d3fEx8ejrq4OWq0WcXFxmDt3rmX2lDqctj4Cak+G6sjHUGRLZMJQ66wNi4mJ4TgCsotAYAgDAVmDoWun3cw1RI7Jni/2RPaCgYDICjhSmWxJqyady8rKQnBwMJRKJdLS0pos12g0mDRpEpRKJYYMGaIz5WlqaiqUSiWCg4Oxe/duo7dJRESWZfQdgSUykQFocZvUMfAXsPH4WVF7MzoQWCITGYAWt0kdH9sBiKzL6EBgqUxkLW2zATOUEQExmW9Yuwpkx5ihjIiI9GqXKSYMrcsMZURE1mf0HYElMpEJIVrcpj7MUEZE1HomZygzZYqJsLAwJCQkIDQ0FM7Ozli1ahWcnJwAQO82W8IMZbZp+rhNBpdt2DatHWtCRPoYalvlFBNkNgwERLbN0LWzXQaU5eTkIDo6Gv3790d0dDS+++47aZ2RI0ciODgYkZGRiIyMxJUrV1q5a0REZIp2GVDm5eWF7du3w8/PD8eOHUN8fLxOdrP09HR2ByUispJ2GVA2cOBAqUzjnMWN01VS++OjHCIC2mlAmZeXl1TGUM5iJycnTJgwAW+++SZkMlmT9288oIw5iy2vuSBh7u0x6BBZV6vaCEzVkLN4zZo10mvp6en49ddfkZubi9zcXGzapP+CMW/ePOTn5yM/Px/e3t7tVWUiog6POYuJiBwccxYTETk4owNB4wFlISEhSEhIkAaUffPNNwCA2bNno6KiAkqlEh9++KHUxbRxzuLG3UQ1Gg3i4+MRERGByMhIKBQK5iwmImpnHFDmwMzdINxWbCwmah9WHVAGMEMZGTZ93CaD/4jI8pihrAPgBZOITMEMZXaCF3sishS7yVBGjokD0Ygsz24ylJkjVWV5eblDDUbr6PsbE/NX6f8dfV8b4752TO2xryanqmzNgDJ/f3+jMpQBMDpDmTlSVTpabyNH2l/ua8fEfW0f7TKgTKVSQa1WQ6PR4OzZs1KGMmO2SURElmWXGcqIiMiMhANZs2aNtavQrhxpf7mvHRP3tX3Y5chiIiIyn3adhpqIiGyPQwSCJUuWQKFQSBPe7dy5U1pmaOoLe9bRp+0ICAhA//79ERkZKXUjvnbtGkaPHo0HHngAo0ePxvXr161cy7abNWsWfHx8dGbiNbR/Qgi8/PLLUCqViIiIwOHDh61V7TbRt68d9ftaUlKCRx99FKGhoQgLC8Nf/3q3+7NNHFurPZRqR2+99ZZ47733mrxeUFAgIiIiRE1NjThz5owIDAwU9fX1Vqih+dTX14vAwEBRXFwsNBqNiIiIEAUFBdaulln169dPlJeX67z2xz/+UaSmpgohhEhNTRX/8z//Y42qmcX3338vDh06JMLCwqTXDO3fjh07xOOPPy7u3LkjfvrpJzF48GCr1Lmt9O1rR/2+lpWViUOHDgkhhKiqqhIPPPCAKCgosIlj6xB3BIY0N/WFvWo8FYiLi4s0bUdHl5mZienTpwMApk+fjm3btlm5Rm33yCOPQC6X67xmaP8yMzORlJQEmUyGoUOHorKyEpcuXWr3OreVvn01xN6/r3369EFUVBSAu4m4QkJCUFpaahPH1mECwcqVKxEREYFZs2ZJt176ps1omPrCXnXEfbqXTCbDmDFjEB0dLY02v3z5Mvr06QMA8PX1xeXLl61ZRbMztH8d9Xh39O/ruXPncOTIEQwZMsQmjm2HCQRxcXEIDw9v8i8zMxO///3vUVxcjKNHj6JPnz74wx/+YO3qkgl++OEHHD58GLt27cKqVatw4MABneUymQwymcxKtbO8jr5/Hf37evPmTUyYMAEfffQRunfvrrPMWsfWbuYaasmePXuMKjd37lw8+eSTAIybNsPedMR9ulfD/vj4+GD8+PHIy8tD7969cenSJfTp0weXLl2Cj4+PlWtpXob2ryMe7969e0v/72jf17q6OkyYMAFTpkzBM888A8A2jm2HuSNoTuPnal9//bXUQ8HQ1Bf2rKNP21FdXY0bN25I/8/OzkZ4eLjO9CYbNmzA008/bc1qmp2h/VOpVNi4cSOEEPj555/Ro0cP6TGDveqo31chBGbPno2QkBC89tpr0us2cWwt1gxtQ6ZOnSrCw8NF//79xVNPPSXKysqkZcuWLROBgYHiwQcfFDt37rRiLc1nx44d4oEHHhCBgYFi2bJl1q6OWRUXF4uIiAgREREhQkNDpf27evWqGDVqlFAqleKxxx4TFRUVVq5p202ePFn4+voKZ2dnoVAoxLp16wzu3507d8Tzzz8vAgMDRXh4uPjnP/9p5dq3jr597ajf19zcXAFA9O/fXwwYMEAMGDBA7NixwyaOLUcWExE5OId4NERERIYxEBAROTgGAiIiB8dAQETk4BgIiIgcHAMBEZGDYyAgInJwDARERA7u/wEK/0KUaQUoiQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6FxGRNerymcXmXn2UiIjkIVtnsf7qo76+vtBqtdi7d69clzMJWwtEZM9sbvXRtr7UiYioJatcfbStPgJzJwK2CIjIVnD1USIiapXV1Cy2FPYfEJGtYyIwAZMEEdkCvhoiIrJzTARERHbOKl8N/Zx7nsNEiYjMRPZEkJ6ejmXLlkGn0yEuLg4rVqww2F5XV4d58+YhKysLHh4eSElJgUqlkjss2XUmUbFfgYgsgaUq7yLsfCYiS2CpSishx6swJhciAqyoVKX+MtQX0YjFDSVyhm4fWBmMyK50eYUyc2OFMiIiecg6fLQ9S1Hr79NWqUoiIpKHrC2C9ixFrdFokJycjOjo6HaXqvT09LSJkUVERF3JWIUyWRNBe5aiXrx4MebOnQu1Wg2lUgmtVnvH86pUqg5XKOusyt+NN7rN/d3PuyQGIiJziDLSL2hzy1CbGxMBEdkKWZehTk9PR3BwMNRqNRISElpsr6urw6xZs6BWqzFs2DCp57qoqAjdu3dHREQEIiIi8PTTT5sjHCIi6gCTXw2ZOmksKCgIp0+fNjUMIiLqJJNbBPqTxrp16yZNGtOXlpaG+fPnA7g9aezIkSOwwjdSREQ2yeQWgSmTxgCgsLAQgwcPhpubGzZs2ICRI0eaGpJN6mxfBfs4iOhOLDqhzMfHB8XFxfDw8EBWVhamTJmC7OxsuLm5tdhXf2ZxWVlZV4dKRGSzTH41ZMqkMRcXF2nyWGRkJIKCgpCXl9fqdeLj45GZmYnMzEz06dPH1LCJiOgXJicC/Ulj9fX10Gq10Gg0Bvs0TxoDYDBprKysDDqdDgBQUFCA/Px8aYE6IiLqGia/GjJl0lhGRgbWrFkDZ2dnODg4IDExEUql0uSbIiKi9rPKCWWD+7jhn1OGt7rN3B2gcnTSmvt8XX1cV8bRFmPntOVOcGvo/LeGGNti7fG35a6cUAYAmzdvhlqtRnBwMD777DNzhENERB1gciJonlB26NAh5OTkYN++fcjJyTHYR39C2QsvvIDly5cDAHJycqDVapGdnY309HQ8++yzUp8BERF1DYtOKEtLS0NsbCxcXFwQEBAAtVqNEydOmBoSERF1gEUnlJWUlGD48OEGx5aUtF55TH8eQf7PTRh3qqL1gLqy6pa5r9XZ83X1cbYah7Wzhvu2hhjbYuXxs0IZERG1StYJZc2dyAUFBdi4cSMAwwllhYWFePHFF/Hggw9izJgxyM/PbzEZjYiI5GVyi8BYFTL9VUkPHDiAdevW4fe//z3OnDkjTSibPn06iouLcfLkSbz55pt44403MHTo0DtekxXKiIg6TrYKZcYmlC1atAhubm4IDAzEkiVLkJSUhJEjRyIoKEiaULZw4UKUlpYiNDQUjY2N6N+/PxwdHe94zQE9m3D06fta3eYct8/UWyIisknGKpSZpY9g4sSJmDhxYovPHBxuv3m655578Mc//hHHjx/H1q1bDfZbtWoVVq1ahaVLl8Lb29sc4RARUQfcFZ3FH3zwATIzM/H1118b3Ud/1ND1n+q6KjQiIptnlpnFrWnPqqQA8MUXX2Djxo3Yv38/XFxcjJ5Pf/VRT1fj+xERUcfIlgjasyrpqVOnsGTJEuzfvx9eXl5yhUJERG2QLRHodyKHhIRg5syZ0qqk+/fvBwC88soruHnzJmbMmIGIiIgWiYKIiORnlauPRqqUOLZ6QqvbOGqIiKh1sq4+SkRE1ouJgIjIzt0Vw0fNqSHpyVY/5ysjIqLW2VwiMMZYggCYJIjIvsn+asiU6mVERCQ/WVsE+gvP+fn5YciQIdBoNAgNDZX20a9eptVqsXz5cqSkpMgZVgtsLRCRPZO1RWBK9TIiIuoasrYITKle5unpKWdo7dZWa6Gz2MogoruJ1XQW6y8698P1ekQn5ls4IhMkWne5OyKyThYpVdmeheea9/Hz8zOoXvZrLFVJRCQPWfsI2rPwnEajQXJyMgAgNTVVql5GRERdQ9YWgbHqZWvWrEFUVBQ0Gg0WL16MuXPnQq1WQ6lUStXL2sJSlUREHWesVKVVLjpnbOGkzhKnXjW6TTF4vdmuQ0RkSca+O83SIkhPT8eyZcug0+kQFxeHFStWGGyvq6vDvHnzkJWVBQ8PD6SkpEClUqGoqAghISEIDg4GAAwfPhyJiYnmCKmFtr7sO3sckwQR2QKTE4Gpk8aCgoJw+vRpU8MgIqJOMrmzmJPGiIism8mJoLVJYyUlJUb30Z80BgCFhYUYPHgwRo0ahW+++cbodbZv346oqChERUWhrKzM1LCJiOgXFp1Q5uPjg+LiYnh4eCArKwtTpkxBdnY23NzcWuyrP48gKsr4hKzO9gUQEdkrkxOBKZPGFAoFXFxcAACRkZEICgpCXl5em1/0dxN2JBORLTA5EehPGvP19YVWq8XevXsN9mmeNBYdHW0waaysrAxKpRKOjo4oKChAfn4+AgMDTQ3prsAkQUTWwuREYMqksYyMDKxZswbOzs5wcHBAYmIilEqlyTd1tzOWJJggiMgSrHNCWWg/nNyz2NJhmB0TARHJSdYJZWQefJ1ERJZglkXnTClHuXnzZqjVagQHB+Ozzz4zRzg2SZx61eg/RESmsOjM4pycHGi1WmRnZ6O0tBRjx45FXl4eHB0dTQ3LrrAlQUSmMDkR6M8sBiDNLNZPBGlpaVi7di2A2zOLly5dCiEE0tLSEBsbCxcXFwQEBECtVuPEiROIjo42NSz6RWdbDEwgRPbD5ERgSjnKkpISDB8+3ODYX89KbqZfoSy3uBpDfnfI1NCpTfz9Etkai1QoMydWKCMikofJncUdmVkMwGBmcXuOJSIieVl0ZrFGo8Hs2bPx4osvorS0FPn5+Rg6dOgdr8kKZUREHWesQplFZxaHhYVh5syZCA0NhZOTE7Zt29auEUMqlcp4hbLqlNY/d5vV2VskIrIJxtZxs86ZxYODkPn1JvOdkEmCiOyAsZnFZplQRkRE1suiM4uLiorQvXt3REREICIiAk8//bQ5wum46hTj/xAR2TjWLCYisnOsWUxEZOess2ZxebWpYRMR0S+ss2bx4KCuC7KtfgKONiIiG2DRmcUuLi7w8PAAYFizmIiIug5rFpuCrQUisgGsWUxEZOfM0kfg4OAAhUIBhUIhLRGxbt06abtCoYCDg0OLf582bRry8vKwY8cOODo6olu3buYI5+7A1gIRWQlWKLMErodERHcRVii7m7AVQUQWYDUVyuwel7toicmRyCyspkKZQanK/GuIivmThSMiy+PfAFFHyFaqsiPzCPz8/DpdoYylKomI5GHyhDL9eQT19fXQarXQaDQG+zTPIwDQokKZVqtFXV0dCgsL212hjIiIzMcqK5R5erqh/wCvVrc5KHqbektERDbJWKlKq6xQ9lCkGt8ce7PVbT2cn+jiaIiIrAMrlBERUauYCIiI7BxLVRIR2TmWqiQisnMsVUlEZOesslTl9etVpoZNRES/sMpSlQ9Fqrs6VCIim8VSlUREds5uSlXWNPw/s5+zrclrbV2Pk96I6G7CUpUm6GxyMXdSYmIhIlPYXKlKOf7P/27H1gcRmYKlKm1cZxMjEwiR/WCpSmqVNbSsOpOs7LX1ZA33bQ0xWoPO/LdrllKVCoUCwcHB0Ol0eOihh+DlZbhE9KVLl7Bp0yacO3cOHh4euPfee1FeXo5z585Bq9VKs4xra2uNlqrUr1CW98NVjIreZGroZPXM/Tdgr39T1nDf1hDj3U+2CmU6nQ7ffvstTp8+DT8/P6jVajz88MMG+9y4cQO9e/eWXg3FxcVJ27y8vKQlJhYvXmz0OqxQRkQkD5PnEVRVVaFbt27SEhMhISHSrOFm9fX1GDlyJABgypQpuHXrFpRKJby9vdHQ0CDt11apSiIikofJLYJevXpJpSZ9fX1x7ty5Fi2Cbt26ISMjA/Hx8fj4449x7733oqKiAmPHjsWWLVsQEREBFxcXXLp0qV2lKj09PaFSqUwNnYjIrhirUGZyInB0dMSIESOkeQRDhw6FUqk0mEfg7u6OyspKaR5B82ziUaNGYeXKldBqtaisrERDQwNqampaXWJCv4+gR48erVbZISIi46Kiolr93CxLTABAXl4eLly4gMjISPj6+mLdunVSEXs/Pz+sXr0a58+fx7fffouamhppiYmNGzfiwoULuHjxIsLCwowuMREfH4/MzExkZmaiT58+poZNRES/MDkR6C8xUV9fD61WKyWAZs1LTABoscSETqcDANmXmCAiotaZZYmJBQsWIDg4GEIIxMTEtFhiYs6cOQgPD4ezszNcXFxw8OBBALeXmHj22Wdx48YNKBQKrFy50qqWmCAisgUmtwh0Oh2Sk5ORm5uLmpoaXL16FTk5OQavhvbs2YNJkyahoaEBSUlJ2LZtGwAgJCQE3t7eqK6uxrlz5/Dee+9JLQQiIuoaFq1QZmxmMRERdR2LVihrz7FERCQvi1Yo6wj94aNlZWUWjoaIyHZYtEJZe45txuGjRETysOjwUY1GA61WK81Mzs/Pb9fMYiIiMh+TXg1VVFRg1qxZqK2tRXh4OLy8vBAXF9di+Gi3bt3wwQcfYOfOnfDz88MXX3wBAHjuuedQUlICNzc3KBQK7Nq1i7UIiIi6mEmJICEhAWPGjMHhw4eRkJCAyspKrFq1CsB/KpRVVFRg8+bNKC4uhkKhQGRkJNzd3aVzpKenG532TERE8jPp1ZD+sND58+fj448/brHPZ599hnHjxkGpVMLd3R3jxo1Denq6KZclIiIzMikRXLt2DT4+PgAAb29vXLt2rcU+dxoiunDhQkRERGD9+vUQQpgSDhERdcIdXw2NHTsWV69ebfH5xo0bDX5uLl7fEXv27IGvry9++uknTJs2De+//z7mzZvX6r76w0dzc3P5OomIqIM6XaGsuWO3NX379sWVK1fg4+ODK1eutChRmZ6ejj//+c+oqamBk5MTVqxYgcuXL+ORRx4BAFy4cAGPPfYYzpw5gyVLluDEiRNGEwErlBERycOkV0P6w0KTk5Px+OOPS9t0Oh2ee+45fPLJJ3B3d8cHH3yAo0eP4vPPP8eECRPQ2NiInj17Yvfu3YiNjUVWVhbCw8NNuxsiIuowhTDhxXx5eTlmzpyJ4uJiDBgwAB9++CGUSiUyMzPx+uuvo76+Hp999hl27tyJV155BQCwZcsWLFy4EDU1Nfiv//ovNDQ0oLi4GCNGjMCBAwfaNXyUFcqIiDpOlgplHh4eOHLkSIvPo6KiMH/+fGl00KJFi+Ds7Izjx49j4cKFAG5XGcvKygIALFiwAJMnT24zCej3EYhuQNx7L7S639OhT5lyS0RENku2CmVdRX+JiZ7KlqUsiYioc2RLBB1ZR4iIiCxHttVH9dcg8vX1hVarxd69e+W6nCQxZ0+rn/OVERFR62RLBE5OTti6dSsmTJgAnU6HRYsWtViD6OTJk5g6dSoqKytx4MABvPbaa8jOzpYlHmMJAmCSICL7ZtKoIUsZEB6IlR+uN9v5mAiIyB5ERUUhMzOzxedW01lMRETysJoKZXLiayMismdsERAR2Tm2CO6ArQUisnVMBCZgkiAiWyB7IkhPT8eyZcug0+kQFxeHFStWGGyvq6vDvHnzkJWVBQ8PD6SkpNjEOkJMEkRkLWRNBM0rkB4+fBh+fn4YMmQINBoNQkNDpX127NgBd3d3nD9/HlqtFsuXL0dKSoqcYVkcJ70R0d1E1kRw4sQJqNVqBAYGAgBiY2ORlpZmkAjS0tKwdu1aAMD06dOxdOlSCCE6XOTGFrTVimgLEwgRmULWRNBamcrjx48b3cfJyQm9evVCeXk5PD095QzNpnQ2gXSltpIVX6MRWZZZEkF7+wG++uorAMDLL78MlUqFoqIi5OTkYMKECXB2dsbw4cONXkN/Geryoh+RNO8v5gidukgSOve8OnscEbVkrFSlyUtM6HQ63H///Qb9APv27UNoaCiOHj2KtWvX4vHHH8eZM2cwYMAAfP/99xBCICUlBUVFRQgPD8fhw4cRHR2NxsZGeHt7o6yszC5fDRERWYLJE8r0+wG6desm9QMA/1mBVKvVYvbs2dBqtVi5ciWOHDmC5vzj5uYmlbtMTU1FTEwMkwARURcy+dVQW/0Aze+C1/oAAAu8SURBVCuQTp06FXPnzkV8fDwGDRqEpqYm7NmzBw8//DCqqqqQkpKC5ORkqFQqfPrpp61eR//VUG5uLgYOHGhq6O1SWF1pdFuAm3uXxEBEZA6ylKpsj4kTJ+K+++5Deno6/Pz8AADu7u549NFH4erqiuLiYnh4eCArKwtTpkwx2kkcHx+P+Ph4AMZX0OusuekfGd0W0sZx7z86w2wxEBHJzVipSpMTQXsqkTXv4+fnh8bGRlRVVcHDwwMKhQIuLi4AgMjISAQFBSEvL89osHebthIIkwQRWQuTE0F7KpFpNBokJycjOjraoB+grKwMSqUSjo6OKCgoQH5+vjTnwNoxSRCRtTA5EbSnEtnixYsxd+5cqNVqKJVKaLVaAEBGRgbWrFkDZ2dnODg4IDExEUql0uSbak1bX8xERPbMKiuUdaaPwBoSAVsKRCQnY9+dXH30LiJHsmJyIaI7YWEaIiI7xxaBjWOnNRHdic0lAmvoCyAiupvYXCKg9utM0mQrgsj2MBFQh/BVE5Ht6dJlqFsrR7l582bs2LEDjo6OeOeddzBhwgRzhEQWwCRBZJ1MTgSmlKPMycmBVqtFdnY2SktLMXbsWOTl5cHR0dHUsOguwyRBdPcyORGYUo4yLS0NsbGxcHFxQUBAANRqNU6cOIHo6Og2r1lYXclOYRvS2WdpLIEw6RB1jKzLULe2j345ypKSEoOqZH5+figpKWn1OvrLUNeVXsO51W+YGjpZuahO/A105hgiW2GsQpnVdBbrL0NNRETmY/LM4o4sQw3AYBnq9hxLRETysugy1BqNBrNnz8aLL76I0tJS5OfnY+jQoXe8pqenpzTqiIiI2ke2CmVOTk5YsGABgoODIYTA6NGjWyxDPWfOHISHh8PZ2RkuLi44ePAgAKBHjx7IycmBm5sbFAoFYmJi2jViSKVSmbVCWWcNW3/Y6Lbjr47rwkiIiO7MWNEvk18N6XQ6JCcnIzc3FzU1Nbh27RpycnKwbt06aDQaAMCePXswadIkNDQ0ICkpCdu2bZOODw4ORl1dHWpra6UEQUREXceiw0etQVv/109EZAssOnwUAAoLCzF48GC4ublhw4YNGDlypKkhdRi/7InInll0+KiPjw+Ki4vh4eGBrKwsTJkyBdnZ2XBzc2uxr/48grKysq4OtcM6k1zYr0BElmDR4aMuLi7w8PAAAERGRiIoKAh5eXmtXic+Ph6ZmZnIzMxEnz59TA2biIh+YXIi0B8+Wl9fD61WK3USN2sePgrAYPhoWVkZdDodAKCgoAD5+flSXwMREXUNswwf3bp1KyZMmACdTodFixa1GD66ePFizJ07F2q1GkqlElqtFgCQkZGBNWvWwNnZGQ4ODkhMTIRSqTT5pmwRh6oSkVwUwlqG7+iJiooy6zwCa+8sZiIgovYw9t1pNWsNtZe1f6mbG1sSRHQnJvcREBGRdWOFMhsgRyvI2DnZiiCyPaxQRl2Cr6iI7l5WWaGMzIP9KUQEWGmFstzcXKOr6JG8otJWWsU5iaglVigjIqJWybrERHp6OoKDg1FQUICNGzcCMFxior6+HsuXL4eTkxNSU1NZoYyIyAJkq1Cm34l84MABrFu3Dr///e9x5swZaYmJ6dOn46uvvsKsWbNw7do1VigjIpKRrBXKWltiYtGiRXBzc0NgYCCWLFmCpKQkjBw5EkFBQdISE+PHj8f8+fORkJCAL7/8UhpGeidtVSibP+X9Dt9D8sdzO3wMEZG1Mda3apY+gokTJ2LixIktPnNwuP3m6Z577sEf//hHHD9+HFu3bjXYb9WqVcjPz8fkyZPx29/+1hzhEBFRB1hNZ7G11SMgIrIWsi0x0Z46BR3BegRERPKQLRG0p04BERFZnmyJQL8TOSQkBDNnzpTqFOzfvx8AcPLkSfj5+eGjjz7CkiVLEBYWJlc4RERkhM3VI+jMqKG2cEQREdkKm6pHUHS+3Oxf+ERE9or1CIiI7BwTARGRnbPKV0Ndqa1XUOw/ICJbIHuLoHnhObVajYSEhBbb6+rqMGvWLKjVagwbNszoMqlERCQPWVsEplQvswZsLRCRLZA1EZhSvUyhUMgZmuyYJIjIWsiaCEypXubp6SlnaBZ1twx9ZUIiIsCKOov1F527WXsF2Zf/auGILKesrMws6y1FRVnf79Bc926teP+8f1Pu3yKlKtuz8FzzPn5+fgbVy35Nv1RlWzOL7YE937893zvA++f9y3P/so4aas/CcxqNBsnJyQCA1NRUqXoZERF1DVlbBMaql61ZswZRUVHQaDRYvHgx5s6dC7VaDaVSKVUvIyKiriF7H0Fr1cvWrVsn/fs999yDjz76qEPnbH5FZK/s+f7t+d4B3j/vX577t8rVR4mIyHy41hARkZ2zqkRwp+UqbM2lS5cwevRohIaGIiwsDH/96+3hnhUVFRg3bhzuu+8+jBs3DpWVlRaOVF46nQ6DBw/G5MmTAQCFhYUYNmwY1Go1Zs2ahfr6egtHKJ8bN25g+vTpGDhwIEJCQnD06FG7ev5/+ctfEBYWhvDwcDz55JOora216ee/aNEieHl5ITw8XPrM2PMWQuAPf/gD1Go1HnzwQXz33Xedvq7VJILm5SoOHTqEnJwc7Nu3Dzk5OZYOS1ZOTk546623kJOTg2PHjmHbtm3IyclBQkICxowZg/z8fIwZM8bmk+Jf//pXhISESD8vX74cL7zwAs6fPw93d3fs2LHDgtHJa9myZXj00UeRm5uL77//HiEhIXbz/EtKSvDOO+8gMzMTZ8+ehU6nk5ahsdXnv2DBAqSnpxt8Zux5Hzp0CPn5+cjPz8f27dvxzDPPdP7Cwkp8++23Yvz48dLPmzZtEps2bbJgRF1Po9GIzz//XNx///2itLRUCCFEaWmpuP/++y0cmXwuXbokYmJixJEjR8SkSZNEU1OT8PDwEA0NDUKIln8XtuTGjRtCpVKJpqYmg8/t5flfvnxZ+Pn5ifLyctHQ0CAmTZok0tPTbf75FxYWirCwMOlnY887Pj5e7N27t9X9OspqWgStLVdRUlJiwYi6VlFREU6dOoVhw4bh2rVr8PHxAQB4e3vj2rVrFo5OPs8//zzefPNNODjc/lMtLy9H79694eR0e8CbLf8dFBYWok+fPli4cCEGDx6MuLg41NTU2M3z9/X1xcsvv4z+/fvDx8cHvXr1QmRkpN08/2bGnrc5vxOtJhHYs5s3b2LatGl4++234ebmZrBNoVDY7AS8Tz75BF5eXoiMjLR0KBbR2NiI7777Ds888wxOnTqFHj16tHgNZMvPv7KyEmlpaSgsLERpaSlqampavDaxN3I9b6tJBO1ZrsIWNTQ0YNq0aXjqqafwxBNPAAD69u2LK1euAACuXLkCLy8vS4Yom3/961/Yv38/VCoVYmNj8c9//hPLli3DjRs30NjYCMC2/w78/Pzg5+eHYcOGAbi9Ou93331nN8//iy++QEBAAPr06QNnZ2c88cQT+Ne//mU3z7+Zsedtzu9Eq0kE7VmuwtYIIbB48WKEhITgxRdflD7XX5YjOTkZjz/+uKVClNXmzZtx+fJlFBUVQavVIiYmBnv27MHo0aORmpoKwLbv39vbG/7+/vjhhx8AAEeOHEFoaKjdPP/+/fvj2LFjuHXrFoQQ0v3by/NvZux5azQavPfeexBC4NixY+jVq5f0CqnDOtuhYQmffvqpuO+++0RgYKDYsGGDpcOR3TfffCMAiAceeEAMGjRIDBo0SHz66afi+vXrIiYmRqjVajFmzBhRXl5u6VBl9+WXX4pJkyYJIYS4cOGCGDJkiAgKChLTp08XtbW1Fo5OPqdOnRKRkZHigQceEI8//rioqKiwq+e/Zs0aERwcLMLCwsScOXNEbW2tTT//2NhY4e3tLZycnISvr69ISkoy+rybmprEs88+KwIDA0V4eLg4efJkp6/LmcVERHbOal4NERGRPJgIiIjsHBMBEZGdYyIgIrJzTARERHaOiYCIyM4xERAR2TkmAiIiO/f/Afdzm6+77/QsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UVltbi5kzZ0Kj0eChhx5CYWGh3CEREZEBWecasqSoLCUlBf7+/jh79iy0Wi2WL1+OXbt2tbjda9n5Nv/Ln4jIXcmaCAyLygBIRWWGiSA9PR2rVq0CAMTGxmLRokUQQkChUMgZmsXamnB4SYmIXIWsicBUUdnx48fNLqNUKtG1a1eUl5ejW7ducoYmO3uOVchx11Nb4mfyI3JNLjMN9ZYtW7BlyxYAwLXOjdjUr9LBEdnXJjPTxwIAHmzjei1pYZs23xcR2YW5MVhZE4ElRWVNy6jVajQ0NKCyshIBAQHNthUfH4/4+Hg5wyUickuyJoLr16/j8OHDCA0NxcKFC/HFF18YFZVt2LABp06dwpQpUxAZGYnp06dj7NixTjM+QETkDmSrI9Dr9bjvvvvw+uuvY926dbh48SJefPFFfPjhh1JRWZcuXXD//fcjISEBR44cgV6vh06nkwaXzbFnq8pf8s6afa1jP41dYiAisgVzrSplSwTff/89Vq1ahf379wMA1q5dCwB47bXXTC6fk5ODRYsW4bvvvrvrtlsqKGuLH0dMa9N693/PabWJyHXYvaDM0mmom6SkpGDSpElyhUNERGY4xV1DaWlp0Ol0yMrKMruM4V1D7EdARGQ7siUCS+4YAoBDhw5hzZo1yMrKkqakNsXwrqGoFm5TNHeZR47LOC1dUuJlIyJyFQ6dhjonJwcJCQnIzMxEUFCQXKEAaPs4ABFRe+fQaaiXLVuGqqoqzJgxAwDQu3dvZGRkyBWS07DnWQsR0d20u2mo2+tf/kwSRGQtc8dOpxgsprvjeAQRyYWJoB1gHQQRWUP2xjREROTcmAiIiNyc7JeGMjMzsXjxYuj1esTFxSExMdHo9draWjz33HPIzs5GQEAAdu3aZbd5hNwdxx2ICHDRVpUkP2e5+6otCamtCY6JkdyVS7aq/CXvrNMcqEhetj44t/V7I0dyacv25MC6FpI1EezZswfHjx+HRqNBXFxcs1aVqamp+Pbbb1FcXAwvLy8sWrTIbKtKw7mGitCABfXmJ7AjN+EsHdFsHUd7fV/kcHbvUKbX65GSkoKJEyciLS0NQ4cOxTPPPNNsua5du2L//v1Qq9UA/n+66l9jhzIiInnIdtfQiRMn0KdPH1RWVqJDhw6YNWsWDh8+3Gziuc6dO0uT07XUqpKIiOQh2xlBSUkJBgwYgCNHjuDChQvo0aMHsrOzsWHDBqPlKisrMWXKFIwZMwZjxowx26rS8NJQXl4e+vXrJ1foRETtkt07lO3evRuZmZl48sknsWTJEty4cQNhYWH44YcfpInnHn74YSiVSsTFxeHIkSOora3FyZMn79qq0tYdyuRQsXCi2df8Pzlgx0iIiO6w+1xDTf0IJk+ejMmTJxtd+1+9erXRsl9++SX0ej1UKtVdkwAREdmWbGMEhv0I6urqoNVqERMTY7RMaWmp9P+MjAxERETIFQ4REZnh0H4ESUlJyMjIgFKphEqlQmpqqlzhEBGRGe2uH4Gz4BgBETkbc8dOTjpHROTmmAiIiNxcu2tM09IlmbZo6TKOrfdFROQIPCMgInJzTARERG6OiYCIyM2xQ9ld2HMcwBVuOXWmGM3F4iyfVXtm7+8Bf9bykvWMoKlD2b59+5Cbm4udO3ciNzfXaBnDDmVLly7F8uXL5QyJiIh+RdZEYNihrGkq6vR0465H6enpmDdvHoA7Hcq+/fZbuGCNGxGRy5L10lBJSQl69eolPf51h7JfL6NUKi3qUJaXl4coV+6e1JbYXeH9OkuMzhKHu7Ln58+fdavYvUOZrbFDGRGRPGS9NNQ0FXWT4uLiZh3KDJdhhzIiIvuT9YzAcCrqkJAQaLVa7Nixw2iZmJgYbNu2DSNGjMDu3bvNdigz1K1bN6e6s4iIyBWY61AmayKwZCrqBQsWYO7cudBoNFCpVNBqtXfdbmhoqNnZR+uTnzH5vFfcTqveCxGRqzM3ttrupqFmIiAiMs0h01BnZmaib9++0Gg0WLduXbPXU1NTERgYiMjISERGRiI5OVnOcIiIyATZLg01FZMdPHgQarUaQ4cORUxMDPr372+03MyZM7Fx40a5wiAioruQ7YzAkmIyIiJyPNkSgalispKSkmbLffXVVxg8eDBiY2ONbjX9tS1btiAqKgpRUVEoKyuTJWYiInfk0IKyqVOn4plnnoG3tzc+/vhjzJs3D//7v/9rclnDgrIhoSqzg8JERNQ6sp0RWFJMFhAQAG9vbwBAXFwcsrOz5QqHiIjMkC0RGBaT1dXVQavVIiYmxmiZ0tJS6f8ZGRmIiIiQKxwiIjJDtktDlhSTJSUlISMjA0qlEiqVCqmpqXKF0+KlJNYYEJE7c8mCsiGhKvzwRrTNtsdEQETuwFxBGTuUgWcLROTeZE0ElhSVGXYo02q1WL58OXbt2iVnWK3CJEFE7Z2sicCwqAyAVFRmmAjS09OxatUqAHc6lC1atAhCiLvOQOrsmECIyFW4TIcyZ9TWWoa2rNdS8mDSISJruEyHMsNWlT9dq8OIzQUOjsjONrexJV9b1yOidschrSpb06FMrVa32KGMrSqJiOQh6zTUlhSVNXUoA2BxhzIiIrIdl+xQxlaVREStZ65VpawFZXerITh69CiWLFmCU6dOQavVIjY21qLtttShzBWInDdNPq944B277Uuu/RGR87J7QZklNQS9e/dGamoq3n//fbnCsJqzHEjliMNZ3hsROZZsicCSGoKmyzseHrIOVcimpQOpM2yPiMgSsiUCS2oIWsPw9tG2NKbhQbZ17Hn5iogcy2XqCAxvH42K4r3xhpjkiMgasiUCS2oIyPVwXIGo/ZEtERjWEISEhECr1WLHjh1y7U7Cv44dp62fPRMIkWM5tDHNP/7xDzzxxBOoqKjA//zP/+Ctt97C6dOn5QqJnBTPMogcyyUb07RUR8AzAmLyIDLNYY1piOyNl6iIWoeJgOg/eImK3BVbVRJZgEmC2jPXbFV5+zLHAshp2DpJMOmQvbFVJZGMXGEaEiYXcplWlYZTTORdvImhC/fJGTqRG+HvkrtwSIeynJwc7N69G0eOHEFcXFyzyuLU1FTk5eUhOjoaXl5eWLRokdltsUMZEZE8ZJv2U6/XIzU1FYMGDUJubi527tyJ7OzsZsmgZ8+eSE5OxsmTJzF//nyzrSqJiEgesk5DPWDAAJw9exYlJSWYMWMGNm3ahEOHDhktd88992Dbtm0YMWKExa0q2aGMiKj1zHUok3Ua6t69e2PJkiWIjo7GjRs3EBYWZjTNBADk5+fjxx9/RFpaGsLDw/HXv/7V5PYMxwg6d+5svkPZTTN3HPnOtPo9ERG5MnMzN8veEWby5MnIz8/H+vXrpSBWr16NmJgYTJ06FYWFhbhx4wbWr1+PgIAA6Q6jX4uPj4dOp4NOp0NgYKDcYRMRuQ3ZEoEl01AHBATA29sbABAXF4fs7Gy5wiEiIjNkSwSG01DX1dVBq9UiJibGaJnS0lLp/xkZGYiIiJArHCIiMsOh01AnJSUhIyMDSqUSKpUKqampcoVjfuwA4PgBEbm1djcNdYsHfHOYCIjIDZg7dso+WExERM6N01ADvGxERG6NZwRERG6OZwR3w7MFImrnXDMR6CvaNihsa0wSRNQOyH5pKDMzE3379oVGo8G6deuavV5bW4uZM2dCo9HgoYceMjtNqsu5ucv8PyIiJ+KaHcpcHedDIiInwg5lzsTeZwtMPEQEF+pQRjLgZSrHaSkJt3XsiWNW1EYuM1hs1Kqy4Cqixv7JwRERWaOt3197r0ftiUNaVVoyA2nTMmq1Gg0NDWY7lLFVJRGRPGwy11BmZiYWL14MvV6PuLg4JCYmAgAaGhpw33334ZlnnsFf/vIXFBYW4v7778euXbvQp08fAICHhwdUKhXUajU8PT0RHh6OL774wtqQiIjIQlYnAr1ej/vuu8/ozqCdO3dKA8LffPMN4uPj4eXlhbi4OKhUKmzcuBFr165FTEwMfHx8MGnSJOTk5EClUkGr1ZptTtPE1q0qG8UNs695KPxsth8iIkeSrVXl3e4Mmjx5MoqLi6Xlc3JykJaWZtSb4Msvv2zVPkNDQ83PPtoG1fV/MftaZ68nbbYfIiJHMteq0upEYMmdQYZSUlIwadIk6XFNTQ2ioqKgVCqRmJiI6dOnm1zPcLC4rKzM2rAtxiRBRO2dXe8aSktLg06nQ1ZWlvRcUVERQkJCcP78eYwdOxaDBg1CeHh4s3UNB4vNZbWWtHRAJyJyZ1ZPMWHJnUEAcOjQIaxZswYZGRlSn+Km9QEgLCwMo0ePRk5OjrUhERFRK1idCCzpTZyTk4OEhARkZGQgKChIer6iogK1tbUAgGvXruG7774zqjomIiL5WX1pyJLexMuWLUNVVRVmzJgBAOjduzcyMjJw5swZJCQkwMPDA42NjUhMTLQ6EdjzElBb98WxBSJyJu2uZ7ErjAUwERCRI7BnMRERmeQycw21J+bOWnimQESOwETgRDjmQESOwEtDRERuziZnBOYmnWtSW1uL5557DtnZ2QgICMCuXbukuYLWrl2LlJQUeHp6IikpCdHR0bYIif7D1oPnPPsgan+sTgTWtKPMzc2FVqvF6dOncfnyZYwfPx75+fnw9PRscZ+N4oZL3B1kL+56yyzHWohsQ/ZJ5wDz7SjT09Mxa9YseHt745577oFGo8GJEycwYsQIa8MiJ9OeE7et56Pi/FbNOcuZrbP8rG0dh00mnVMoFOjbty/0ej0efPBBo+phALh06RLee+89nDlzBgEBAejUqRPKy8tx5swZaLVaqVl9TU0NSkpKTO7HcNK5/J+uYNSI96wNndotZ/pu2DoWZ3pvrkyOz9FZftbm15OtQ5ler8exY8dw8uRJqNVqaDQa/Pa3vzVa5saNG/Dz85MuDcXFxUmvBQUF4eTJkwCABQsWmN0PO5QREcnD6ruGKisr0aFDB4SFhaFDhw6IiIhAeXm50TJ1dXV45JFHAADTp0/H7du3oVKp0KNHD9TX10vLmZuwjoiI5GP1GUHXrl1RW1uLCxcuICQkBGfOnGl2RtChQwccPXoU8fHx+Prrr9GpUydcv34d48ePx/vvv4/IyEh4e3vj0qVLGDZs2F33aesOZURE7kC2DmWenp4YOXKkNOncsGHDoFKpjCad8/f3R0VFBTQaDVQqldScftSoUXjttdeg1WpRUVGB+vp6VFdXw9fXt9l+DMcIOnfubNMOZURE7sBcLxeb9CMAgPz8fJw7dw5DhgxBSEgIVq9eLU1HrVar8cYbb+Ds2bM4duwYqqurERAQAG9vb6xZswbnzp1DUVERBgwYgPz8fJP7iY+Ph06ng06nQ2BgoLVhExHRf9ilH0FMTAy2bdsGANi9ezfGjh0LhUKBsrIy6PV6AMD58+dRUFBw18b1RERkWzbpRzB//nz07dsXQgiMHTu2WT+COXPmYODAgfDy8oK3tze++eYbAMDRo0fx+9//Hjdu3IBCocBrr70GlUpl9ZsiIiLLWX1GoNfrsW3bNuTl5aG6uhpXrlxBbm6u0aWh7du34/HHH0d9fT2Sk5OxadMmAEBERAR69OiBmzdv4syZM/jss8+kMwQiIrIPqxOBYWVxhw4dpMpiQ+np6Zg3bx6AO5XF3377bYuVxUREZD9WJ4KSkhL06tVLeqxWq5tVBxsuo1Qq0bVrV5SXl1u0LhERyctl+hEY3j5aVlbm4GiIiNoPm9w+eunSJemxqepgw2UaGhpQWVmJgIAAi9ZtwttHiYjk4dDbR2NiYqDVaqXK5IKCAosqi4mIyHasujR0/fp1zJw5EzU1NRg4cCCCgoIQFxfX7PbRDh06IC0tDZ9++inUajUOHToEAHjppZdQUlICX19fKBQKbN269a69CIiIyLasSgTr1q3DuHHjcPDgQaxbtw4VFRVYsWIFAGD16tUA7iSLtWvX4uLFi1AoFBgyZAj8/f2lbWRmZpoteyYiIvlZdWnI8LbQefPm4euvv262zP79+zFhwgSoVCr4+/tjwoQJyMzMtGa3RERkQ1YlgqtXryI4OBgA0KNHD1y9erXZMne7RfT5559HZGQk3nnnHQghrAmHiIja4K6XhsaPH48rV640e37NmjVGjxUKBfqOuYQAAA1+SURBVBQKRat2vn37doSEhODWrVt46qmn8Pnnn+O5554zuazh7aN5eXm8nERE1Ept7lDWNLBrSvfu3VFaWorg4GCUlpY2a1EJ3Ll19MiRI9Lj4uJijB49WnoNALp06YLZs2fjxIkTZhMBO5QREcnDqktDhreFbtu2DdOmTWu2THR0NA4cOICKigpUVFTgwIEDiI6ORkNDg9Qgob6+Hnv27MHAgQOtCYeIiNpAIay4MF9eXo6nn34aFy9eRJ8+ffDFF19ApVJBp9Nh8+bNSE5OBgB8+umneO+9Ow2VV6xYgeeffx7V1dV49NFHUV9fD71ej/Hjx2PDhg0W3T7KDmVERK1nrkOZVYnAUaKiosx2KNucu93k8y/2f1bOkIiInJ65Y6fVlcVEROTaXGbSOWuZO1MAeLZARO7NJmcEmZmZ6Nu3LzQaDdatW9fs9Q0bNqB///4YPHgwxo0bh6KiIuk1T09PREZGIjIystkcRUREJD+rzwj0ej1eeuklHDx4EGq1GkOHDkVMTAz69+8vLfPAAw9Ap9OhU6dO+Oijj/Dqq69i165dAICOHTvi5MmT1oZBRERtZJcOZWPGjEGnTp0AAMOHD0dxcbG1uyUiIhux+ozA1BQSx48fN7t8SkoKJk2aJD2uqalBVFQUlEolEhMTMX36dJPrydmYhuMHROTO7DpYnJaWBp1Oh6ysLOm5oqIihISE4Pz58xg7diwGDRqE8PDwZusaVhZzegkiItuxOhFY2mXs0KFDWLNmDbKysuDt7W20PgCEhYVh9OjRyMnJMZkIHIVnC0TU3tmlQ1lOTg4SEhKQkZFhNB9RRUUFamtrAQDXrl3Dd999ZzTITERE8rP6jECpVGLjxo2Ijo6GXq/HCy+80KxD2bJly1BVVYUZM2YAAHr37o2MjAycOXMGCQkJ8PDwQGNjIxITEy1KBGU111v8S91eeLZARO2BTcYIPDw8pGmom+YKaupQBgB79+7Fc889h+zsbAQEBCApKQkAMHLkSMyePRspKSnw9PSEWq22RThOoS2JismDiBzBLnUEKSkp8Pf3x9mzZ6HVarF8+XLs2rULubm50Gq1OH36NC5fvozx48cjPz/fbfsW8wyDiBzB6kRgWEcAQKojMEwE6enpWLVqFQAgNjYWixYtghAC6enpmDVrFry9vXHPPfdAo9HgxIkTGDFihLVhtTv2vhTGxEPkPuxSR2C4jFKpRNeuXVFeXo6SkhIMHz7caF3DNpbkOM4wBtMemEuornD25woxkm24zKRzhgVl5YU/I/m5DxwcEdHdJaP139O2rGNvrhAjNdfmVpV3Y0kdQdMyarUaDQ0NqKysREBAgMU1CABbVRIRycUudQSGLS13796NsWPHQqFQICYmBlqtFrW1tbhw4QIKCgowbNgwa0MiIqJWsEsdwYIFCzB37lxoNBqoVCpotVoAwIABA/D000+jf//+UCqV2LRpE1tVksUu3Kww+fw9vv52joTINbhNq0pyH3MzvzT5/OePzbBzJESuwdyx02UGi4ksZS5BAM6TJFwhRnIfNkkEmZmZWLx4MfR6PeLi4pCYmGj0em1trVFl8a5duxAaGorCwkJERESgb9++AO70Kti8ebMtQiJqtbYenF39oM4zK3JoZTEAhIeHs0MZmdXSQdYZtmdvrh4/OSeHVhYTAa5xcGtrjK7w3trC1c+CyJhDK4sB4MKFC3jggQfg6+uLd999F4888oi1IRGRDciR/HiJzTk5dLA4ODgYFy9eREBAALKzszF9+nScPn0avr6+zZaVs1Vle2brX8q2/iKT5drz52jP9+auCaktHFpZrFAopG5lQ4YMQXh4OPLz8022omSrSiL31Z6TozOwOhEYVhaHhIRAq9Vix44dRss0VRaPGDHCqLK4rKwMKpUKnp6eOH/+PAoKCqSxBjImx18p/OUid+Us331nOftwaGXx0aNHsXLlSnh5ecHDwwObN2+GSqWy+k2RfJzlF4jIEdry/bf370xb9sfKYgew9ReD1+2JyBJn3vijfJXFbS0oA4C1a9dKrSqTkpIQHR1ti5DcCg/2RGQNtqq0grNc3yMisgZbVd4FC4mIqL1zmVaVhnUEeXl5vIWUiKiVZOtQZi/sUEZEJA+rO5S1pqAMQJtbVRIRkTwcWlAWExOD2bNn4+WXX8bly5ctblXJDmVERK1nrkOZS7aqDA0NdYo6gofeOWj2teNvTrBjJEREd2dubJUFZVZgIiAiV2Lu2Gn1GAFwp6Csb9++0Gg0WLduXbPXN2zYgP79+2Pw4MEYN24cioqKpNc8PT0RGRmJyMhIxMTE2CIcIiJqBbsUlD3wwAPQ6XTo1KkTPvroI7z66qtSh7KOHTuyQxkRkQNZfUZgWFDWoUMHqaDM0JgxY9CpUycAd/oSFxcXW7tbIiKyEbsUlBlKSUnBpEmTpMc1NTWIioqCUqlEYmIipk+fbnI9d2hM09KYQ0s4HkFE1rBrQVlaWhp0Oh2ysrKk54qKihASEoLz589j7NixGDRoEMLDw5ut254a07T1gE9EJAe7dCgDgEOHDmHNmjXIysqSupI1rQ8AYWFhGD16NHJyckwmAkdp60GbB3sichVWjxEYFpTV1dVBq9U2u/snJycHCQkJyMjIQFBQkPR8RUUFamtrAQDXrl3Dd999ZzTITERE8rNLQdmyZctQVVWFGTPuTM3cu3dvZGRk4MyZM0hISICHhwcaGxuRmJjIREBEZGduU1DmrpdqWhpIZkEckXtxaEFZbW0tZs6cCY1Gg4ceeshoKtS1a9dCo9Ggb9++2L9/vy3CISKiVmh3Hcrc9S9/c+z5efAMg8g1sUMZtQoTLVH7ww5lZFJU+mtOvT0iaj12KCMiIpPYoYyIyM2xQxkRkZtwmw5l86Z/bvL5bV/PbfubJCJqB9ymQxkTARGRabIWlJlzt0Kz1NRUBAYGSh3KkpOT5QyHiIhMkO2uIUsKzQBg5syZ2Lhxo1xhSMydKQA8WyAi9ybbGYElncuIiMjxZEsEpgrNTBWLffXVVxg8eDBiY2ONbiX9tS1btiAqKgpRUVHttkMZEZEjyDpGcDdTp05FYWEhTp06hQkTJmDevHlml42Pj4dOp4NOp0NgYKAdoyQiat9kSwSWFIsFBARI3cri4uKQnZ0tVzhERGSGbIPFlhSalZaWIjg4GACQkZGBiIgIucJpEQeSicidyZYILCk0S0pKQkZGBpRKJVQqFVJTU+UKh4iIzHCbgrK24hkBEbUXDikoA6zrXkZERPKTdRpqa7qXOQuOHxBReydrIrCme5lCoTC73cKz5Ta/BNQWTBJE1B7Imgis6V7WrVs3OUOTXVsSFZMHETmCy3QoM2xVWVVTitPFHzo4ItuLirLteyorK2PxnQX4OVmGn5NlnPlzckirytZ0L1Or1Ubdy37NsFVlS3cN0f/j52QZfk6W4edkGVf8nGS9a8iwqKyurg5arRYxMTFGyzR1LwNg1L2MiIjsQ9YzAmu6lxERkX3IPkYwefJkTJ482ei51atXS///zW9+gy+//LJV22y6REQt4+dkGX5OluHnZBlX/JxcsrKYiIhsx6HTUBMRkeO5VCK423QVdEdoaCgGDRqEyMhIREVFOTocp/LCCy8gKCgIAwcOlJ67fv06JkyYgHvvvRcTJkxARUWFAyN0PFOf0apVqxASEiL1F//mm28cGKFzuHTpEsaMGYP+/ftjwIAB+PDDO7d/u+L3yWUSQdN0Ffv27UNubi527tyJ3NxcR4fltA4fPoyTJ0+63G1scps/fz4yMzONnlu3bh3GjRuHgoICjBs3zu3/yDD1GQHA0qVLcfLkSZw8ebLZuJ87UiqVWL9+PXJzc/HDDz9g06ZNyM3Ndcnvk8skAvZAJlt49NFHoVKpjJ5LT0+XuuPNmzcPX3/9tSNCcxqmPiNqLjg4GA8++CAAoEuXLoiIiEBJSYlLfp9cJhFY2gOZAIVCgYkTJ2LIkCFSNTaZd/XqValBUo8ePXD16lUHR+ScNm7ciMGDB+OFF15wicsd9lRYWIicnBw89NBDLvl9cplEQJb7+9//jn/+85/Yt28fNm3ahKNHjzo6JJehUChY0GjC7373O5w7dw4nT55EcHAwXnnlFUeH5DSqqqrw1FNP4c9//jN8fX2NXnOV75PLJAJLpqugO5o+l6CgIDzxxBM4ceKEgyNybt27d0dpaSmAO+1Tg4KCHByR8+nevTs8PT3h4eGBhQsX8jv1H/X19Xjqqafw7LPP4sknnwTgmt8nl0kElkxXQUB1dTVu3bol/f/AgQNGd39Qc4bTnGzbtg3Tpk1zcETOp+nABgB//etf+Z0CIITAggULEBERgZdffll63iW/T8KF7N27V9x7770iLCxMvPvuu44OxymdO3dODB48WAwePFj079+fn9OvzJo1S/To0UMolUoREhIikpOTxbVr18TYsWOFRqMR48aNE+Xl5Y4O06FMfUZz5swRAwcOFIMGDRJTp04Vly9fdnSYDve3v/1NABCDBg0S999/v7j//vvF3r17XfL7xMpiIiI35zKXhoiISB5MBEREbo6JgIjIzTEREBG5OSYCIiI3x0RAROTmmAiIiNwcEwERkZv7P/EZtIfiVSv5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 16:03:58 INFO     Importing CSV file 1 from folder 1\n",
            "2020-05-04 16:04:00 INFO     Importing CSV file 2 from folder 1\n",
            "2020-05-04 16:04:01 INFO     NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
            "  out=out, **kwargs)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2020-05-04 16:04:07 INFO     All saved.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}